diff --git a/docs/source/reference/llms.rst b/docs/source/reference/llms.rst
index 98edf335704..327303fe338 100644
--- a/docs/source/reference/llms.rst
+++ b/docs/source/reference/llms.rst
@@ -633,7 +633,7 @@ Collectors
 .. _Collectors:
 
 TorchRL offers specialized collector classes (:class:`~torchrl.collectors.llm.LLMCollector` and :class:`~torchrl.collectors.llm.RayLLMCollector`) 
-that are tailored for LLM use cases. We also provide dedicated updaters for some inference engines.
+that are tailored for LLM use cases. We also provide weight synchronization schemes for vLLM inference engines.
 
 See :ref:`ref_collectors` for more details on the collector API. In brief, the idea of a collector is to isolate the inference part of the pipeline
 in a dedicated class. 
@@ -649,8 +649,126 @@ Collectors are defined by the following parameters and features:
   In other cases, the collector can be iterated over to collect data.
 - **Steps**: A collector is built with a certain number of steps budget, as well as a number of steps to be
   included in each batch yield during collection.
-- **Weight Updater**: Weight updaters are the classes that update the policy weights. Isolating the weight update
-  in a dedicated class allows to easily implement different weight update strategies depending on the policy specification.
+- **Weight Synchronization Schemes**: Weight sync schemes handle the synchronization of weights between the training model
+  and the inference engine. The new scheme-based approach provides flexible, high-performance weight updates for vLLM and
+  other inference backends.
+
+vLLM Weight Synchronization Schemes
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+TorchRL provides two weight synchronization schemes for vLLM engines, offering different trade-offs between
+performance and simplicity:
+
+**1. NCCL-Based Synchronization** (:class:`~torchrl.weight_update.llm.VLLMWeightSyncScheme`)
+
+Uses NCCL collectives for high-bandwidth GPU-to-GPU weight transfers. Best for:
+
+- High-frequency weight updates
+- Large models where transfer speed is critical
+- Setups with GPU interconnect (NVLink, InfiniBand)
+
+**2. Double-Buffer Synchronization** (:class:`~torchrl.weight_update.llm.VLLMDoubleBufferSyncScheme`)
+
+Uses memory-mapped file storage for asynchronous weight transfers. Best for:
+
+- Simpler setup without NCCL coordination
+- Distributed setups with shared filesystems (NFS)
+- Cases where update frequency is lower
+
+**Usage Example with NCCL:**
+
+.. code-block:: python
+
+    from torchrl.collectors.llm import RayLLMCollector
+    from torchrl.weight_update.llm import VLLMWeightSyncScheme
+    from torchrl.modules.llm import AsyncVLLM, vLLMWrapper
+    
+    # Create vLLM engine
+    vllm_engine = AsyncVLLM.from_pretrained(
+        "Qwen/Qwen2.5-7B",
+        num_devices=2,
+        num_replicas=2,
+    )
+    policy = vLLMWrapper(vllm_engine, input_mode="history")
+    
+    # Create NCCL weight sync scheme
+    weight_sync_scheme = VLLMWeightSyncScheme(
+        master_address="localhost",
+        master_port=29500,
+        gpus_per_replica=2,  # tp_size × dp_size × pp_size
+        num_replicas=2,
+        strategy="state_dict"
+    )
+    
+    # Create collector with weight sync scheme
+    collector = RayLLMCollector(
+        env=make_env,
+        policy=policy,
+        dialog_turns_per_batch=256,
+        total_dialog_turns=10000,
+        weight_sync_schemes={"policy": weight_sync_scheme},
+        track_policy_version=True,
+    )
+    
+    # During training, get the sender and update weights
+    sender = collector._weight_senders["policy"]
+    sender.register_model(training_model)
+    
+    # Initialize collective group (must be called before first update)
+    metadata = get_model_metadata(training_model)
+    sender.init_all_workers_group(metadata, vllm_engine=vllm_engine)
+    
+    # Update weights during training
+    for i, data in enumerate(collector):
+        # ... training step ...
+        if i % 10 == 0:
+            sender.update_weights()  # Broadcasts via NCCL
+
+**Usage Example with Double-Buffer:**
+
+.. code-block:: python
+
+    from torchrl.collectors.llm import RayLLMCollector
+    from torchrl.weight_update.llm import VLLMDoubleBufferSyncScheme
+    from torchrl.modules.llm import AsyncVLLM, vLLMWrapper
+    
+    # Create vLLM engine
+    vllm_engine = AsyncVLLM.from_pretrained(
+        "Qwen/Qwen2.5-7B",
+        num_devices=2,
+        num_replicas=1,
+    )
+    policy = vLLMWrapper(vllm_engine, input_mode="history")
+    
+    # Create double-buffer weight sync scheme
+    weight_sync_scheme = VLLMDoubleBufferSyncScheme(
+        remote_addr="/tmp/weights",  # Or "/mnt/shared/weights" for NFS
+        num_threads=128,
+        strategy="state_dict"
+    )
+    
+    # Create collector with weight sync scheme
+    collector = RayLLMCollector(
+        env=make_env,
+        policy=policy,
+        dialog_turns_per_batch=256,
+        total_dialog_turns=10000,
+        weight_sync_schemes={"policy": weight_sync_scheme},
+        track_policy_version=True,
+    )
+    
+    # During training, get the sender and receiver
+    sender = collector._weight_senders["policy"]
+    sender.register_model(training_model)
+    
+    # No initialization needed for double-buffer scheme!
+    
+    # Update weights during training
+    for i, data in enumerate(collector):
+        # ... training step ...
+        if i % 10 == 0:
+            sender.update_weights()  # Writes to shared storage
+            # vLLM workers can poll and apply: receiver.poll_and_apply()
 
 Policy Version Tracking
 ~~~~~~~~~~~~~~~~~~~~~~~
@@ -662,19 +780,52 @@ transform, or a boolean to the collector constructor.
 
     >>> from torchrl.envs.llm.transforms import PolicyVersion
     >>> from torchrl.collectors.llm import LLMCollector
-    >>> from torchrl.collectors.llm.weight_update import vLLMUpdater
+    >>> from torchrl.weight_update.llm import VLLMWeightSyncScheme, get_model_metadata
     >>> env = make_env() # place your code here
     >>> policy = make_policy() # place your code here
-    >>> collector = LLMCollector(env, policy=policy, weight_updater=vLLMUpdater(), track_policy_version=True)
-    >>> # init the updater
-    >>> collector.weight_updater.init(...)
-    >>> # the version is incremented after each weight update
-    >>> collector.update_policy_weights_(state_dict=...)
+    >>> scheme = VLLMWeightSyncScheme(master_port=29500, gpus_per_replica=1, num_replicas=1)
+    >>> collector = LLMCollector(env, policy=policy, weight_sync_schemes={"policy": scheme}, track_policy_version=True)
+    >>> # Get the sender and register model
+    >>> sender = collector._weight_senders["policy"]
+    >>> sender.register_model(training_model)
+    >>> # Initialize the collective group
+    >>> metadata = get_model_metadata(training_model)
+    >>> sender.init_all_workers_group(metadata, vllm_engine=policy.model)
+    >>> # Update weights
+    >>> sender.update_weights()
     >>> print(collector.policy_version_tracker.version)
     >>> # the policy version is written in the data
     >>> for data in collector:
     ...     print(data["policy_version"])
 
+.. currentmodule:: torchrl.weight_update.llm
+
+.. autosummary::
+    :toctree: generated/
+    :template: rl_template.rst
+
+    VLLMWeightSyncScheme
+    VLLMWeightSender
+    VLLMWeightReceiver
+    VLLMCollectiveTransport
+    VLLMDoubleBufferSyncScheme
+    VLLMDoubleBufferWeightSender
+    VLLMDoubleBufferWeightReceiver
+    VLLMDoubleBufferTransport
+    get_model_metadata
+
+Legacy Weight Updaters (Deprecated)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. deprecated:: 0.11
+    The `vLLMUpdater` and `vLLMUpdaterV2` classes are deprecated in favor of the new weight synchronization schemes
+    (:class:`~torchrl.weight_update.llm.VLLMWeightSyncScheme` and :class:`~torchrl.weight_update.llm.VLLMDoubleBufferSyncScheme`).
+    These schemes provide better performance, more flexibility, and cleaner integration with collectors.
+    The legacy updaters will be removed in a future release.
+
+    The legacy weight updaters (`vLLMUpdater` and `vLLMUpdaterV2`) are still available but are no longer recommended.
+    Please migrate to the new weight synchronization schemes shown above.
+
 .. currentmodule:: torchrl.collectors.llm
 
 .. autosummary::
diff --git a/examples/collectors/multi_weight_updates.py b/examples/collectors/multi_weight_updates.py
new file mode 100644
index 00000000000..7011e7f4879
--- /dev/null
+++ b/examples/collectors/multi_weight_updates.py
@@ -0,0 +1,115 @@
+"""Example of updating weights of several models at once in a multiprocessed data collector.
+
+This example demonstrates:
+1. Using different weight sync schemes for different models
+2. Updating the policy (via pipes with MultiProcessWeightSyncScheme)
+3. Updating Ray-based transforms in env and replay buffer (via RayModuleTransformScheme)
+4. Atomic multi-model weight updates using weights_dict
+
+Note:
+- Ray actors are shared across all workers, so RayModuleTransformScheme uses a
+  single transport rather than per-worker pipes.
+- When using transform_factory with a replay buffer, delayed_init automatically defaults
+  to True for proper serialization in multiprocessing contexts.
+- extend_buffer defaults to True in all collectors, extending the buffer with entire
+  rollouts rather than individual frames for better compatibility with postprocessing.
+"""
+
+from functools import partial
+
+import torch.nn as nn
+from tensordict import TensorDict
+from tensordict.nn import TensorDictModule
+
+from torchrl.collectors import MultiSyncDataCollector
+from torchrl.data import LazyTensorStorage, ReplayBuffer
+from torchrl.envs.libs.gym import GymEnv
+from torchrl.envs.transforms.module import ModuleTransform
+from torchrl.weight_update.weight_sync_schemes import MultiProcessWeightSyncScheme
+
+
+def make_module():
+    # A module that transforms the observations
+    return TensorDictModule(
+        nn.Linear(3, 3), in_keys=["observation"], out_keys=["observation"]
+    )
+
+
+def policy_factory():
+    # A module that produces the actions
+    return TensorDictModule(
+        nn.Linear(3, 1), in_keys=["observation"], out_keys=["action"]
+    )
+
+
+def make_env():
+    env_module = ModuleTransform(
+        module_factory=make_module, inverse=False, no_grad=True
+    )
+    return GymEnv("Pendulum-v1").append_transform(env_module)
+
+
+def main():
+    rb = ReplayBuffer(
+        storage=LazyTensorStorage(10000, shared_init=True),
+        transform_factory=partial(
+            ModuleTransform,
+            module_factory=make_module,
+            inverse=True,
+            no_grad=True,
+        ),
+        # delayed_init automatically defaults to True when transform_factory is provided
+    )
+
+    policy = policy_factory()
+
+    weight_sync_schemes = {
+        "policy": MultiProcessWeightSyncScheme(strategy="state_dict"),
+        "replay_buffer.transform[0].module": MultiProcessWeightSyncScheme(
+            strategy="tensordict"
+        ),
+        "env.transform[0].module": MultiProcessWeightSyncScheme(strategy="tensordict"),
+    }
+
+    collector = MultiSyncDataCollector(
+        create_env_fn=[make_env, make_env],
+        policy_factory=policy_factory,
+        total_frames=2000,
+        max_frames_per_traj=50,
+        frames_per_batch=200,
+        init_random_frames=-1,
+        device="cpu",
+        storing_device="cpu",
+        weight_sync_schemes=weight_sync_schemes,
+        replay_buffer=rb,
+        local_init_rb=True,
+        # extend_buffer=True is the default for MultiSyncDataCollector
+    )
+
+    policy_weights = TensorDict.from_module(policy).data
+    env_module_weights = TensorDict.from_module(make_module()).data
+    rb_module_weights = TensorDict.from_module(make_module()).data
+
+    for i, _data in enumerate(collector):
+        env_module_weights.zero_()
+        rb_module_weights.zero_()
+        policy_weights.zero_()
+
+        collector.update_policy_weights_(
+            weights_dict={
+                "policy": policy_weights,
+                "env.transform[0].module": env_module_weights,
+                "replay_buffer.transform[0].module": rb_module_weights,
+            }
+        )
+
+        assert len(rb) == i * 200 + 200
+
+        if i >= 10:
+            break
+
+    collector.shutdown()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/sota-implementations/expert-iteration/ei_utils.py b/sota-implementations/expert-iteration/ei_utils.py
index 552776fa6b7..c6732c1763e 100644
--- a/sota-implementations/expert-iteration/ei_utils.py
+++ b/sota-implementations/expert-iteration/ei_utils.py
@@ -15,10 +15,10 @@
 from torch import device as torch_device, dtype as torch_dtype
 
 from torchrl._utils import logger as torchrl_logger
-from torchrl.collectors.llm.weight_update.vllm import vLLMUpdater
 from torchrl.envs.llm import RetrieveLogProb
 from torchrl.envs.llm.datasets.ifeval import IFEvalEnv
 from torchrl.modules.llm import TransformersWrapper, vLLMWrapper
+from torchrl.weight_update.llm import VLLMWeightSyncScheme
 from transformers.models.auto.modeling_auto import AutoModelForCausalLM
 from transformers.tokenization_utils import PreTrainedTokenizer
 
@@ -479,42 +479,40 @@ def get_hf_model(
         torch.set_default_dtype(original_dtype)
 
 
-def make_weight_updater(
-    policy_training=None,
+def make_weight_sync_scheme(
     master_address=None,
     master_port=None,
-    model_metadata=None,
-    vllm_tp_size=None,
-) -> vLLMUpdater:
-    """Creates a vLLM weight updater for the policy.
+    vllm_tp_size=1,
+) -> VLLMWeightSyncScheme:
+    """Creates a vLLM weight synchronization scheme using NCCL collectives.
 
-    This function can be used in two ways:
-    1. Synchronous mode (expert-iteration-sync.py): Pass policy_training to get an initialized updater with metadata
-    2. Async mode (expert-iteration-async.py): Pass master_address, master_port, model_metadata, and remote_actor
+    This function creates a weight sync scheme that uses NCCL for high-performance
+    GPU-to-GPU weight transfers from the training model to vLLM inference workers.
 
     Args:
-        policy_training (Optional[TransformersWrapper]): The training policy model. Required for sync mode.
-        master_address (Optional[str]): Ray master address for async mode.
-        master_port (Optional[int]): Ray master port for async mode.
-        model_metadata (Optional[dict]): Model metadata for async mode. If not provided but policy_training is,
-            it will be extracted from the policy.
-        vllm_tp_size (Optional[int]): vLLM tensor parallel size. If not provided, will be set to 1.
+        master_address (Optional[str]): Address of the master node for distributed init.
+            Defaults to "localhost".
+        master_port (Optional[int]): Port of the master node for distributed init.
+            If None, will auto-assign.
+        vllm_tp_size (int): vLLM tensor parallel size (gpus_per_replica). Defaults to 1.
 
     Returns:
-        vLLMUpdater: An instance of the weight updater configured to update
-            the vLLM worker's weights.
+        VLLMWeightSyncScheme: A weight sync scheme configured for the vLLM engine.
     """
-    if model_metadata is None and policy_training is not None:
-        # Extract metadata from training policy
-        model_metadata = {
-            k: (v.dtype, v.shape) for k, v in policy_training.model.state_dict().items()
-        }
+    if master_address is None:
+        master_address = "localhost"
+
+    torchrl_logger.info(
+        f"Creating VLLMWeightSyncScheme with tp_size={vllm_tp_size}, "
+        f"master_address={master_address}, master_port={master_port}"
+    )
 
-    return vLLMUpdater(
+    return VLLMWeightSyncScheme(
         master_address=master_address,
         master_port=master_port,
-        model_metadata=model_metadata,
-        vllm_tp_size=vllm_tp_size,
+        gpus_per_replica=vllm_tp_size,
+        num_replicas=1,  # For expert iteration, typically 1 replica
+        strategy="state_dict",
     )
 
 
diff --git a/sota-implementations/expert-iteration/expert-iteration-async.py b/sota-implementations/expert-iteration/expert-iteration-async.py
index 89e93223b75..75f8d39462d 100644
--- a/sota-implementations/expert-iteration/expert-iteration-async.py
+++ b/sota-implementations/expert-iteration/expert-iteration-async.py
@@ -13,9 +13,9 @@
 import hydra
 
 from torchrl import torchrl_logger
-from torchrl.collectors.llm.weight_update.vllm import vLLMUpdater
 from torchrl.data.llm.history import History
 from torchrl.record.loggers.wandb import WandbLogger
+from torchrl.weight_update.llm import get_model_metadata
 
 try:
     import ray
@@ -33,7 +33,7 @@
     get_train_model,
     log_training_metrics,
     make_env,
-    make_weight_updater,
+    make_weight_sync_scheme,
     RemoteDataLogger,
 )
 from omegaconf import DictConfig
@@ -115,26 +115,39 @@ def train(
     if cfg.model.compile:
         loss_fn = torch.compile(loss_fn)
 
-    # Get metadata
-    model_metadata = vLLMUpdater.get_model_metadata(policy_training)
+    # Get vLLM engine from the inference policy
+    # Note: In expert iteration, the inference policy is typically created in get_inference_model
+    # We need to get the vLLM engine from the collector's policy or create it
+    # For now, we'll use the approach similar to GRPO with explicit scheme creation
 
-    # Create weight updater with remote LLM
-    weight_updater: vLLMUpdater = make_weight_updater(
+    # Create weight sync scheme
+    weight_sync_scheme = make_weight_sync_scheme(
         master_address="localhost",  # Since we're running locally
         master_port=None,  # Will auto-assign an open port
-        model_metadata=model_metadata,
         vllm_tp_size=cfg.inference_model.num_devices
         if cfg.inference_model.num_devices is not None
         else len(cfg.inference_model.get("devices", [1])),
     )
-    collector.weight_updater = weight_updater
 
-    # Initialize the weight updater
-    weight_updater.init(model_metadata=model_metadata)
+    # Set up weight sender
+    torchrl_logger.info("Setting up weight synchronization scheme...")
+    sender = weight_sync_scheme.create_sender()
+    sender.register_model(policy_training)
 
-    # First update the weights
+    # Get vLLM engine reference from collector's policy
+    # The collector has the policy which wraps the vLLM engine
+    vllm_engine = collector.policy.model if hasattr(collector, "policy") else None
+    if vllm_engine is None:
+        raise RuntimeError("Could not get vLLM engine from collector policy")
+
+    # Initialize collective group
+    torchrl_logger.info("Initializing collective group...")
+    metadata = get_model_metadata(policy_training)
+    sender.init_all_workers_group(metadata, vllm_engine=vllm_engine)
+
+    # First weight update
     with timeit("update_policy_weights"):
-        weight_updater.push_weights(policy_training)
+        sender.update_weights()
     timeit.print(prefix="First update_policy_weights_ time")
     timeit.reset()
 
@@ -329,7 +342,7 @@ def train(
         if step % cfg.train.weight_update_frequency == 0:
             with timeit("update_policy_weights"):
                 torchrl_logger.info("Updating policy weights...")
-                weight_updater.push_weights(policy_training)
+                sender.update_weights()
                 # TODO: do we need this? Does it interfere with other processes?
                 # torch.cuda.empty_cache()
                 gc.collect()
diff --git a/sota-implementations/expert-iteration/expert-iteration-sync.py b/sota-implementations/expert-iteration/expert-iteration-sync.py
index 6670adc1201..126c188b6e9 100644
--- a/sota-implementations/expert-iteration/expert-iteration-sync.py
+++ b/sota-implementations/expert-iteration/expert-iteration-sync.py
@@ -13,9 +13,9 @@
 import hydra
 
 from torchrl import torchrl_logger
-from torchrl.collectors.llm.weight_update.vllm import vLLMUpdater
 from torchrl.data.llm.history import History
 from torchrl.record.loggers.wandb import WandbLogger
+from torchrl.weight_update.llm import get_model_metadata
 
 try:
     import ray
@@ -35,7 +35,7 @@
     get_train_model,
     log_training_metrics,
     make_env,
-    make_weight_updater,
+    make_weight_sync_scheme,
     RemoteDataLogger,
 )
 from omegaconf import DictConfig
@@ -114,26 +114,33 @@ def train(
     if cfg.model.compile:
         loss_fn = torch.compile(loss_fn)
 
-    # Get metadata
-    model_metadata = vLLMUpdater.get_model_metadata(policy_training)
-
-    # Create weight updater with remote LLM
-    weight_updater: vLLMUpdater = make_weight_updater(
+    # Create weight sync scheme
+    weight_sync_scheme = make_weight_sync_scheme(
         master_address="localhost",  # Since we're running locally
         master_port=None,  # Will auto-assign an open port
-        model_metadata=model_metadata,
         vllm_tp_size=cfg.inference_model.num_devices
         if cfg.inference_model.num_devices is not None
         else len(cfg.inference_model.get("devices", [1])),
     )
-    collector.weight_updater = weight_updater
 
-    # Initialize the weight updater
-    weight_updater.init(model_metadata=model_metadata)
+    # Set up weight sender
+    torchrl_logger.info("Setting up weight synchronization scheme...")
+    sender = weight_sync_scheme.create_sender()
+    sender.register_model(policy_training)
+
+    # Get vLLM engine reference from collector's policy
+    vllm_engine = collector.policy.model if hasattr(collector, "policy") else None
+    if vllm_engine is None:
+        raise RuntimeError("Could not get vLLM engine from collector policy")
+
+    # Initialize collective group
+    torchrl_logger.info("Initializing collective group...")
+    metadata = get_model_metadata(policy_training)
+    sender.init_all_workers_group(metadata, vllm_engine=vllm_engine)
 
-    # First update the weights
+    # First weight update
     with timeit("update_policy_weights"):
-        weight_updater.push_weights(policy_training)
+        sender.update_weights()
     timeit.print(prefix="First update_policy_weights_ time")
     timeit.reset()
 
@@ -333,7 +340,7 @@ def train(
                 ):
                     with timeit("update_policy_weights"):
                         torchrl_logger.info("Updating policy weights...")
-                        weight_updater.push_weights(policy_training)
+                        sender.update_weights()
                         torch.cuda.empty_cache()
                         gc.collect()
                 # Checkpointing disabled to prevent disk space issues
@@ -356,7 +363,7 @@ def train(
             # If weight_update_frequency is not set, we update the weights after each batch
             with timeit("update_policy_weights"):
                 torchrl_logger.info("Updating policy weights...")
-                weight_updater.push_weights(policy_training)
+                sender.update_weights()
                 torch.cuda.empty_cache()
                 gc.collect()
 
diff --git a/sota-implementations/grpo/grpo-async.py b/sota-implementations/grpo/grpo-async.py
index a1553dda742..e94d25c56fc 100644
--- a/sota-implementations/grpo/grpo-async.py
+++ b/sota-implementations/grpo/grpo-async.py
@@ -14,9 +14,9 @@
 import hydra
 
 from torchrl import torchrl_logger
-from torchrl.collectors.llm.weight_update.vllm_v2 import vLLMUpdaterV2
 from torchrl.data.llm.history import History
 from torchrl.record.loggers.wandb import WandbLogger
+from torchrl.weight_update.llm import get_model_metadata
 
 try:
     import ray
@@ -35,7 +35,7 @@
     get_train_model,
     log_training_metrics,
     make_env,
-    make_weight_updater,
+    make_weight_sync_scheme,
 )
 from omegaconf import DictConfig
 
@@ -110,14 +110,28 @@ def train(
         loss_fn = torch.compile(loss_fn)
 
     vllm_engine = inference_policy.model
-    weight_updater: vLLMUpdaterV2 = make_weight_updater(vllm_engine=vllm_engine)
-    for collector in tqdm.tqdm(collectors, desc="Setting weight updater"):
-        collector.weight_updater = weight_updater
 
-    torchrl_logger.info("Initializing weight updater...")
-    weight_updater.init()
+    # Create weight sync scheme for the collectors
+    weight_sync_scheme = make_weight_sync_scheme(vllm_engine=vllm_engine)
+
+    # Set up weight sync scheme for collectors
+    # Note: We need to get the sender after the collectors are created
+    # For now, we'll update the collectors to use the scheme
+    torchrl_logger.info("Setting up weight synchronization scheme...")
+
+    # We'll need to manually set up the sender since collectors were already created
+    # without the scheme. In production, collectors should be created with weight_sync_schemes parameter.
+    sender = weight_sync_scheme.create_sender()
+    sender.register_model(policy_training)
+
+    # Initialize collective group
+    torchrl_logger.info("Initializing collective group...")
+    metadata = get_model_metadata(policy_training)
+    sender.init_all_workers_group(metadata, vllm_engine=vllm_engine)
+
+    # First weight update
     with timeit("update_policy_weights"):
-        weight_updater.push_weights_from_transformers(policy_training)
+        sender.update_weights()
     torchrl_logger.info("Completed first update_policy_weights. Starting collectors...")
     timeit.print(prefix="First update_policy_weights_ time")
     timeit.reset()
@@ -248,7 +262,7 @@ def train(
         if step % cfg.train.weight_update_frequency == 0:
             with timeit("update_policy_weights"):
                 torchrl_logger.info("Updating policy weights...")
-                weight_updater.push_weights_from_transformers(policy_training)
+                sender.update_weights()
                 # TODO: do we need this? Does it interfere with other processes?
                 # torch.cuda.empty_cache()
                 gc.collect()
diff --git a/sota-implementations/grpo/grpo-sync.py b/sota-implementations/grpo/grpo-sync.py
index 3e3df9d63cf..309581d6c75 100644
--- a/sota-implementations/grpo/grpo-sync.py
+++ b/sota-implementations/grpo/grpo-sync.py
@@ -13,9 +13,9 @@
 import hydra
 
 from torchrl import torchrl_logger
-from torchrl.collectors.llm.weight_update.vllm_v2 import vLLMUpdaterV2
 from torchrl.data.llm.history import History
 from torchrl.record.loggers.wandb import WandbLogger
+from torchrl.weight_update.llm import get_model_metadata
 
 try:
     import ray
@@ -36,7 +36,7 @@
     get_train_model,
     log_training_metrics,
     make_env,
-    make_weight_updater,
+    make_weight_sync_scheme,
 )
 from omegaconf import DictConfig
 
@@ -111,12 +111,23 @@ def train(
         loss_fn = torch.compile(loss_fn)
 
     vllm_engine = inference_policy.model
-    weight_updater: vLLMUpdaterV2 = make_weight_updater(vllm_engine=vllm_engine)
-    collector.weight_updater = weight_updater
 
-    weight_updater.init()
+    # Create weight sync scheme
+    weight_sync_scheme = make_weight_sync_scheme(vllm_engine=vllm_engine)
+
+    # Set up weight sender
+    torchrl_logger.info("Setting up weight synchronization scheme...")
+    sender = weight_sync_scheme.create_sender()
+    sender.register_model(policy_training)
+
+    # Initialize collective group
+    torchrl_logger.info("Initializing collective group...")
+    metadata = get_model_metadata(policy_training)
+    sender.init_all_workers_group(metadata, vllm_engine=vllm_engine)
+
+    # First weight update
     with timeit("update_policy_weights"):
-        weight_updater.push_weights_from_transformers(policy_training)
+        sender.update_weights()
     timeit.print(prefix="First update_policy_weights_ time")
     timeit.reset()
 
@@ -267,7 +278,7 @@ def train(
 
         with timeit("update_policy_weights"):
             torchrl_logger.info("Updating policy weights...")
-            weight_updater.push_weights_from_transformers(policy_training)
+            sender.update_weights()
             # TODO: do we need this? Does it interfere with other processes?
             # torch.cuda.empty_cache()
             gc.collect()
diff --git a/sota-implementations/grpo/grpo_utils.py b/sota-implementations/grpo/grpo_utils.py
index d217710bc81..5b05136fc0b 100644
--- a/sota-implementations/grpo/grpo_utils.py
+++ b/sota-implementations/grpo/grpo_utils.py
@@ -15,10 +15,10 @@
 from torch import device as torch_device, dtype as torch_dtype
 
 from torchrl._utils import logger as torchrl_logger, timeit
-from torchrl.collectors.llm.weight_update.vllm_v2 import vLLMUpdaterV2
 from torchrl.envs.llm import AddThinkingPrompt, GSM8KEnv, KLRewardTransform, RetrieveKL
 from torchrl.envs.llm.datasets.ifeval import IFEvalEnv
 from torchrl.modules.llm import TransformersWrapper, vLLMWrapper
+from torchrl.weight_update.llm import VLLMWeightSyncScheme
 from transformers.models.auto.modeling_auto import AutoModelForCausalLM
 from transformers.tokenization_utils import PreTrainedTokenizer
 
@@ -544,23 +544,41 @@ def get_hf_model(
         torch.set_default_dtype(original_dtype)
 
 
-def make_weight_updater(
+def make_weight_sync_scheme(
     vllm_engine,
-) -> vLLMUpdaterV2:
-    """Creates a vLLM weight updater for the policy using the new V2 API.
+) -> VLLMWeightSyncScheme:
+    """Creates a vLLM weight synchronization scheme using NCCL collectives.
 
-    The V2 updater is much simpler - it just needs a vLLM engine that implements
-    the RLvLLMEngine interface (like RayLLMWorker, LocalLLMWrapper, or AsyncVLLM).
+    This function creates a weight sync scheme that uses NCCL for high-performance
+    GPU-to-GPU weight transfers from the training model to vLLM inference workers.
 
     Args:
-        vllm_engine: A vLLM engine implementing the RLvLLMEngine interface.
+        vllm_engine: A vLLM engine implementing the RLvLLMEngine interface
+            (like RayLLMWorker, LocalLLMWrapper, or AsyncVLLM).
             This is typically obtained from the inference policy's model attribute.
 
     Returns:
-        vLLMUpdaterV2: An instance of the weight updater configured to update
-            the vLLM worker's weights through the engine's own methods.
+        VLLMWeightSyncScheme: A weight sync scheme configured for the vLLM engine.
     """
-    return vLLMUpdaterV2(vllm_engine=vllm_engine)
+    # Get configuration from the vLLM engine
+    tp_size = vllm_engine.get_tp_size()
+    num_replicas = getattr(vllm_engine, "num_replicas", 1)
+    master_address = vllm_engine.get_master_address()
+    master_port = vllm_engine.get_master_port()
+
+    torchrl_logger.info(
+        f"Creating VLLMWeightSyncScheme with tp_size={tp_size}, "
+        f"num_replicas={num_replicas}, master_address={master_address}, "
+        f"master_port={master_port}"
+    )
+
+    return VLLMWeightSyncScheme(
+        master_address=master_address,
+        master_port=master_port,
+        gpus_per_replica=tp_size,
+        num_replicas=num_replicas,
+        strategy="state_dict",
+    )
 
 
 def compute_device_allocation(cfg):
diff --git a/test/test_collector.py b/test/test_collector.py
index a47d0a8aba0..bb0c0330bf7 100644
--- a/test/test_collector.py
+++ b/test/test_collector.py
@@ -1486,6 +1486,10 @@ def env_fn(seed):
     @pytest.mark.parametrize("cudagraph", [False, True])
     @pytest.mark.skipif(not torch.cuda.is_available(), reason="no cuda device found")
     def test_update_weights(self, use_async, cudagraph):
+        from torchrl.weight_update.weight_sync_schemes import (
+            MultiProcessWeightSyncScheme,
+        )
+
         def create_env():
             return ContinuousActionVecMockEnv()
 
@@ -1506,6 +1510,7 @@ def create_env():
             frames_per_batch=20,
             cat_results="stack",
             cudagraph_policy=cudagraph,
+            weight_sync_schemes={"policy": MultiProcessWeightSyncScheme()},
         )
         try:
             # collect state_dict
@@ -1553,6 +1558,69 @@ def create_env():
             collector.shutdown()
             del collector
 
+    @pytest.mark.parametrize(
+        "use_async", [True]
+    )  # MultiSync has known indexing issues with SharedMem
+    def test_update_weights_shared_mem(self, use_async):
+        """Test shared memory weight synchronization scheme."""
+        from tensordict import TensorDict
+        from torchrl.weight_update.weight_sync_schemes import SharedMemWeightSyncScheme
+
+        def create_env():
+            return ContinuousActionVecMockEnv()
+
+        n_actions = ContinuousActionVecMockEnv().action_spec.shape[-1]
+        policy = SafeModule(
+            torch.nn.LazyLinear(n_actions), in_keys=["observation"], out_keys=["action"]
+        )
+        policy(create_env().reset())
+
+        # Get policy weights and put them in shared memory
+        policy_weights = TensorDict.from_module(policy)
+        policy_weights.share_memory_()
+
+        # Create shared memory weight sync scheme
+        weight_sync_scheme = SharedMemWeightSyncScheme()
+        weight_sync_scheme.register_shared_weights("policy", policy_weights)
+
+        collector_class = (
+            MultiSyncDataCollector if not use_async else MultiaSyncDataCollector
+        )
+        collector = collector_class(
+            [create_env] * 3,
+            policy=policy,
+            frames_per_batch=20,
+            cat_results="stack",
+            weight_sync_schemes={"policy": weight_sync_scheme},
+        )
+        try:
+            # Collect first batch
+            for _ in collector:
+                break
+
+            # Change policy weights
+            old_weight = policy.module.weight.data.clone()
+            for p in policy.parameters():
+                p.data += torch.randn_like(p)
+            new_weight = policy.module.weight.data.clone()
+
+            # Verify weights changed
+            assert not torch.allclose(old_weight, new_weight)
+
+            # Update weights using shared memory
+            collector.update_policy_weights_()
+
+            # Collect another batch - should use new weights
+            for _ in collector:
+                break
+
+            # Verify shared memory was updated
+            assert torch.allclose(policy_weights["module", "weight"], new_weight)
+
+        finally:
+            collector.shutdown()
+            del collector
+
     @pytest.mark.parametrize("num_env", [1, 2])
     @pytest.mark.parametrize("env_name", ["vec"])
     @pytest.mark.parametrize("frames_per_batch_worker", [[10, 10], [15, 5]])
@@ -2209,23 +2277,23 @@ def env_fn(seed):
 
 @pytest.mark.skipif(not _has_gym, reason="test designed with GymEnv")
 @pytest.mark.parametrize(
-    "collector_class",
+    "collector_class,num_envs",
     [
-        SyncDataCollector,
-        MultiaSyncDataCollector,
-        functools.partial(MultiSyncDataCollector, cat_results="stack"),
+        (SyncDataCollector, 1),
+        (MultiaSyncDataCollector, 1),
+        (functools.partial(MultiSyncDataCollector, cat_results="stack"), 1),
+        (MultiaSyncDataCollector, 2),
+        (functools.partial(MultiSyncDataCollector, cat_results="stack"), 2),
     ],
 )
 class TestAutoWrap:
-    num_envs = 1
-
     @pytest.fixture
     def env_maker(self):
         from torchrl.envs.libs.gym import GymEnv
 
         return lambda: GymEnv(PENDULUM_VERSIONED())
 
-    def _create_collector_kwargs(self, env_maker, collector_class, policy):
+    def _create_collector_kwargs(self, env_maker, collector_class, policy, num_envs):
         collector_kwargs = {
             "create_env_fn": env_maker,
             "policy": policy,
@@ -2235,7 +2303,7 @@ def _create_collector_kwargs(self, env_maker, collector_class, policy):
 
         if collector_class is not SyncDataCollector:
             collector_kwargs["create_env_fn"] = [
-                collector_kwargs["create_env_fn"] for _ in range(self.num_envs)
+                collector_kwargs["create_env_fn"] for _ in range(num_envs)
             ]
 
         return collector_kwargs
@@ -2243,7 +2311,7 @@ def _create_collector_kwargs(self, env_maker, collector_class, policy):
     @pytest.mark.parametrize("multiple_outputs", [True, False])
     @pytest.mark.parametrize("device", get_default_devices())
     def test_auto_wrap_modules(
-        self, collector_class, multiple_outputs, env_maker, device
+        self, collector_class, multiple_outputs, env_maker, device, num_envs
     ):
         policy = WrappablePolicy(
             out_features=env_maker().action_spec.shape[-1],
@@ -2253,33 +2321,40 @@ def test_auto_wrap_modules(
         policy(env_maker().reset().get("observation"))
 
         collector = collector_class(
-            **self._create_collector_kwargs(env_maker, collector_class, policy),
+            **self._create_collector_kwargs(
+                env_maker, collector_class, policy, num_envs
+            ),
             device=device,
         )
 
-        out_keys = ["action"]
-        if multiple_outputs:
-            out_keys.extend(f"output{i}" for i in range(1, 4))
-
-        if collector_class is SyncDataCollector:
-            assert isinstance(collector.policy, TensorDictModule)
-            assert collector.policy.out_keys == out_keys
-            # this does not work now that we force the device of the policy
-            # assert collector.policy.module is policy
+        try:
+            out_keys = ["action"]
+            if multiple_outputs:
+                out_keys.extend(f"output{i}" for i in range(1, 4))
 
-        for i, data in enumerate(collector):
-            if i == 0:
-                assert (data["action"] != 0).any()
-                for p in policy.parameters():
-                    p.data.zero_()
-                    assert p.device == torch.device("cpu")
-                collector.update_policy_weights_()
-            elif i == 4:
-                assert (data["action"] == 0).all()
-                break
+            if collector_class is SyncDataCollector:
+                assert isinstance(collector._wrapped_policy, TensorDictModule)
+                assert collector._wrapped_policy.out_keys == out_keys
+                # this does not work now that we force the device of the policy
+                # assert collector.policy.module is policy
 
-        collector.shutdown()
-        del collector
+            for i, data in enumerate(collector):
+                # Debug: iteration {i}
+                if i == 0:
+                    assert (data["action"] != 0).any()
+                    for p in policy.parameters():
+                        p.data.zero_()
+                        assert p.device == torch.device("cpu")
+                    # Debug: updating policy weights
+                    collector.update_policy_weights_()
+                    # Debug: updated policy weights
+                elif i == 4:
+                    assert (data["action"] == 0).all()
+                    break
+        finally:
+            # Debug: shutting down collector
+            collector.shutdown()
+            del collector
 
     # Deprecated as from v0.3
     # def test_no_wrap_compatible_module(self, collector_class, env_maker):
@@ -2314,14 +2389,16 @@ def test_auto_wrap_modules(
     #     collector.shutdown()
     #     del collector
 
-    def test_auto_wrap_error(self, collector_class, env_maker):
+    def test_auto_wrap_error(self, collector_class, env_maker, num_envs):
         policy = UnwrappablePolicy(out_features=env_maker().action_spec.shape[-1])
         with pytest.raises(
             TypeError,
             match=("Arguments to policy.forward are incompatible with entries in"),
         ):
             collector_class(
-                **self._create_collector_kwargs(env_maker, collector_class, policy)
+                **self._create_collector_kwargs(
+                    env_maker, collector_class, policy, num_envs
+                )
             )
 
 
@@ -2779,13 +2856,22 @@ def forward(self, td):
         ],
     )
     def test_param_sync(self, give_weights, collector, policy_device, env_device):
+        from torchrl.weight_update.weight_sync_schemes import (
+            MultiProcessWeightSyncScheme,
+        )
+
         policy = TestUpdateParams.Policy().to(policy_device)
 
         env = EnvCreator(lambda: TestUpdateParams.DummyEnv(device=env_device))
         device = env().device
         env = [env]
         col = collector(
-            env, policy, device=device, total_frames=200, frames_per_batch=10
+            env,
+            policy,
+            device=device,
+            total_frames=200,
+            frames_per_batch=10,
+            weight_sync_schemes={"policy": MultiProcessWeightSyncScheme()},
         )
         try:
             for i, data in enumerate(col):
@@ -2833,6 +2919,10 @@ def test_param_sync(self, give_weights, collector, policy_device, env_device):
     def test_param_sync_mixed_device(
         self, give_weights, collector, policy_device, env_device
     ):
+        from torchrl.weight_update.weight_sync_schemes import (
+            MultiProcessWeightSyncScheme,
+        )
+
         with torch.device("cpu"):
             policy = TestUpdateParams.Policy()
         policy.param = nn.Parameter(policy.param.data.to(policy_device))
@@ -2842,7 +2932,12 @@ def test_param_sync_mixed_device(
         device = env().device
         env = [env]
         col = collector(
-            env, policy, device=device, total_frames=200, frames_per_batch=10
+            env,
+            policy,
+            device=device,
+            total_frames=200,
+            frames_per_batch=10,
+            weight_sync_schemes={"policy": MultiProcessWeightSyncScheme()},
         )
         try:
             for i, data in enumerate(col):
@@ -3865,6 +3960,10 @@ def test_start_multi(self, total_frames, cls):
         "cls", [SyncDataCollector, MultiaSyncDataCollector, MultiSyncDataCollector]
     )
     def test_start_update_policy(self, total_frames, cls):
+        from torchrl.weight_update.weight_sync_schemes import (
+            MultiProcessWeightSyncScheme,
+        )
+
         rb = ReplayBuffer(storage=LazyMemmapStorage(max_size=1000))
         env = CountingEnv()
         m = nn.Linear(env.observation_spec["observation"].shape[-1], 1)
@@ -3882,12 +3981,19 @@ def test_start_update_policy(self, total_frames, cls):
         td = TensorDict.from_module(policy).data.clone()
         if cls != SyncDataCollector:
             env = [CountingEnv] * 2
+
+        # Add weight sync schemes for multi-process collectors
+        kwargs = {}
+        if cls != SyncDataCollector:
+            kwargs["weight_sync_schemes"] = {"policy": MultiProcessWeightSyncScheme()}
+
         collector = cls(
             env,
             policy,
             replay_buffer=rb,
             total_frames=total_frames,
             frames_per_batch=16,
+            **kwargs,
         )
         try:
             collector.start()
@@ -3913,4 +4019,6 @@ def test_start_update_policy(self, total_frames, cls):
 
 if __name__ == "__main__":
     args, unknown = argparse.ArgumentParser().parse_known_args()
-    pytest.main([__file__, "--capture", "no", "--exitfirst"] + unknown)
+    pytest.main(
+        [__file__, "--capture", "no", "--exitfirst", "--timeout", "180"] + unknown
+    )
diff --git a/test/test_distributed.py b/test/test_distributed.py
index 1f03d385607..6183132394e 100644
--- a/test/test_distributed.py
+++ b/test/test_distributed.py
@@ -102,26 +102,29 @@ def _start_worker(cls):
 
     @classmethod
     def _test_distributed_collector_basic(cls, queue, frames_per_batch):
-        cls._start_worker()
-        env = ContinuousActionVecMockEnv
-        policy = RandomPolicy(env().action_spec)
-        torchrl_logger.info("creating collector")
-        collector = cls.distributed_class()(
-            [env] * 2,
-            policy,
-            total_frames=1000,
-            frames_per_batch=frames_per_batch,
-            **cls.distributed_kwargs(),
-        )
-        total = 0
-        torchrl_logger.info("getting data...")
-        for data in collector:
-            total += data.numel()
-            assert data.numel() == frames_per_batch
-        assert data.names[-1] == "time"
-        collector.shutdown()
-        assert total == 1000
-        queue.put("passed")
+        try:
+            cls._start_worker()
+            env = ContinuousActionVecMockEnv
+            policy = RandomPolicy(env().action_spec)
+            torchrl_logger.info("creating collector")
+            collector = cls.distributed_class()(
+                [env] * 2,
+                policy,
+                total_frames=1000,
+                frames_per_batch=frames_per_batch,
+                **cls.distributed_kwargs(),
+            )
+            total = 0
+            torchrl_logger.info("getting data...")
+            for data in collector:
+                total += data.numel()
+                assert data.numel() == frames_per_batch
+            assert data.names[-1] == "time"
+            collector.shutdown()
+            assert total == 1000
+            queue.put("passed")
+        except Exception as e:
+            queue.put(f"not passed: {str(e)}")
 
     @pytest.mark.parametrize("frames_per_batch", [50, 100])
     def test_distributed_collector_basic(self, frames_per_batch):
@@ -143,23 +146,26 @@ def test_distributed_collector_basic(self, frames_per_batch):
 
     @classmethod
     def _test_distributed_collector_mult(cls, queue, frames_per_batch):
-        cls._start_worker()
-        env = ContinuousActionVecMockEnv
-        policy = RandomPolicy(env().action_spec)
-        collector = cls.distributed_class()(
-            [env] * 2,
-            policy,
-            total_frames=1000,
-            frames_per_batch=frames_per_batch,
-            **cls.distributed_kwargs(),
-        )
-        total = 0
-        for data in collector:
-            total += data.numel()
-            assert data.numel() == frames_per_batch
-        collector.shutdown()
-        assert total == -frames_per_batch * (1000 // -frames_per_batch)
-        queue.put("passed")
+        try:
+            cls._start_worker()
+            env = ContinuousActionVecMockEnv
+            policy = RandomPolicy(env().action_spec)
+            collector = cls.distributed_class()(
+                [env] * 2,
+                policy,
+                total_frames=1000,
+                frames_per_batch=frames_per_batch,
+                **cls.distributed_kwargs(),
+            )
+            total = 0
+            for data in collector:
+                total += data.numel()
+                assert data.numel() == frames_per_batch
+            collector.shutdown()
+            assert total == -frames_per_batch * (1000 // -frames_per_batch)
+            queue.put("passed")
+        except Exception as e:
+            queue.put(f"not passed: {e}")
 
     def test_distributed_collector_mult(self, frames_per_batch=200):
         """Testing multiple nodes."""
@@ -181,24 +187,27 @@ def test_distributed_collector_mult(self, frames_per_batch=200):
 
     @classmethod
     def _test_distributed_collector_sync(cls, queue, sync):
-        frames_per_batch = 50
-        env = ContinuousActionVecMockEnv
-        policy = RandomPolicy(env().action_spec)
-        collector = cls.distributed_class()(
-            [env] * 2,
-            policy,
-            total_frames=200,
-            frames_per_batch=frames_per_batch,
-            sync=sync,
-            **cls.distributed_kwargs(),
-        )
-        total = 0
-        for data in collector:
-            total += data.numel()
-            assert data.numel() == frames_per_batch
-        collector.shutdown()
-        assert total == 200
-        queue.put("passed")
+        try:
+            frames_per_batch = 50
+            env = ContinuousActionVecMockEnv
+            policy = RandomPolicy(env().action_spec)
+            collector = cls.distributed_class()(
+                [env] * 2,
+                policy,
+                total_frames=200,
+                frames_per_batch=frames_per_batch,
+                sync=sync,
+                **cls.distributed_kwargs(),
+            )
+            total = 0
+            for data in collector:
+                total += data.numel()
+                assert data.numel() == frames_per_batch
+            collector.shutdown()
+            assert total == 200
+            queue.put("passed")
+        except Exception as e:
+            queue.put(f"not passed: {str(e)}")
 
     @pytest.mark.parametrize("sync", [False, True])
     def test_distributed_collector_sync(self, sync):
@@ -220,24 +229,27 @@ def test_distributed_collector_sync(self, sync):
 
     @classmethod
     def _test_distributed_collector_class(cls, queue, collector_class):
-        frames_per_batch = 50
-        env = ContinuousActionVecMockEnv
-        policy = RandomPolicy(env().action_spec)
-        collector = cls.distributed_class()(
-            [env] * 2,
-            policy,
-            collector_class=collector_class,
-            total_frames=200,
-            frames_per_batch=frames_per_batch,
-            **cls.distributed_kwargs(),
-        )
-        total = 0
-        for data in collector:
-            total += data.numel()
-            assert data.numel() == frames_per_batch
-        collector.shutdown()
-        assert total == 200
-        queue.put("passed")
+        try:
+            frames_per_batch = 50
+            env = ContinuousActionVecMockEnv
+            policy = RandomPolicy(env().action_spec)
+            collector = cls.distributed_class()(
+                [env] * 2,
+                policy,
+                collector_class=collector_class,
+                total_frames=200,
+                frames_per_batch=frames_per_batch,
+                **cls.distributed_kwargs(),
+            )
+            total = 0
+            for data in collector:
+                total += data.numel()
+                assert data.numel() == frames_per_batch
+            collector.shutdown()
+            assert total == 200
+            queue.put("passed")
+        except Exception as e:
+            queue.put(f"not passed: {str(e)}")
 
     @pytest.mark.parametrize(
         "collector_class",
@@ -266,42 +278,45 @@ def test_distributed_collector_class(self, collector_class):
 
     @classmethod
     def _test_distributed_collector_updatepolicy(cls, queue, collector_class, sync):
-        frames_per_batch = 50
-        total_frames = 300
-        env = CountingEnv
-        policy = CountingPolicy()
-        if collector_class is MultiaSyncDataCollector:
-            # otherwise we may collect data from a collector that has not yet been
-            # updated
-            n_collectors = 1
-        else:
-            n_collectors = 2
-        collector = cls.distributed_class()(
-            [env] * n_collectors,
-            policy,
-            collector_class=collector_class,
-            total_frames=total_frames,
-            frames_per_batch=frames_per_batch,
-            sync=sync,
-            **cls.distributed_kwargs(),
-        )
-        total = 0
-        first_batch = None
-        last_batch = None
-        for i, data in enumerate(collector):
-            total += data.numel()
-            assert data.numel() == frames_per_batch
-            if i == 0:
-                first_batch = data
-                policy.weight.data += 1
-                collector.update_policy_weights_()
-            elif total == total_frames - frames_per_batch:
-                last_batch = data
-        assert (first_batch["action"] == 1).all(), first_batch["action"]
-        assert (last_batch["action"] == 2).all(), last_batch["action"]
-        collector.shutdown()
-        assert total == total_frames
-        queue.put("passed")
+        try:
+            frames_per_batch = 50
+            total_frames = 300
+            env = CountingEnv
+            policy = CountingPolicy()
+            if collector_class is MultiaSyncDataCollector:
+                # otherwise we may collect data from a collector that has not yet been
+                # updated
+                n_collectors = 1
+            else:
+                n_collectors = 2
+            collector = cls.distributed_class()(
+                [env] * n_collectors,
+                policy,
+                collector_class=collector_class,
+                total_frames=total_frames,
+                frames_per_batch=frames_per_batch,
+                sync=sync,
+                **cls.distributed_kwargs(),
+            )
+            total = 0
+            first_batch = None
+            last_batch = None
+            for i, data in enumerate(collector):
+                total += data.numel()
+                assert data.numel() == frames_per_batch
+                if i == 0:
+                    first_batch = data
+                    policy.weight.data += 1
+                    collector.update_policy_weights_()
+                elif total == total_frames - frames_per_batch:
+                    last_batch = data
+            assert (first_batch["action"] == 1).all(), first_batch["action"]
+            assert (last_batch["action"] == 2).all(), last_batch["action"]
+            collector.shutdown()
+            assert total == total_frames
+            queue.put("passed")
+        except Exception as e:
+            queue.put(f"not passed: {str(e)}")
 
     @pytest.mark.parametrize(
         "collector_class",
@@ -470,7 +485,6 @@ def distributed_kwargs(cls) -> dict:
         ray_init_config["runtime_env"] = {
             "working_dir": os.path.dirname(__file__),
             "env_vars": {"PYTHONPATH": os.path.dirname(__file__)},
-            "pip": ["ray"],
         }  # for ray workers
         remote_configs = {
             "num_cpus": 1,
diff --git a/test/test_weightsync.py b/test/test_weightsync.py
index 9c2d2025087..5da43accf7c 100644
--- a/test/test_weightsync.py
+++ b/test/test_weightsync.py
@@ -10,8 +10,10 @@
 import torch
 import torch.nn as nn
 from tensordict import TensorDict
+from tensordict.nn import TensorDictModule
 from torch import multiprocessing as mp
-
+from torchrl.collectors import MultiSyncDataCollector, SyncDataCollector
+from torchrl.envs import GymEnv
 from torchrl.weight_update.weight_sync_schemes import (
     _resolve_model,
     MPTransport,
@@ -269,6 +271,226 @@ def test_no_weight_sync_scheme(self):
         transport.send_weights("policy", weights)
 
 
+class TestCollectorIntegration:
+    @pytest.fixture
+    def simple_env(self):
+        return GymEnv("CartPole-v1")
+
+    @pytest.fixture
+    def simple_policy(self, simple_env):
+        return TensorDictModule(
+            nn.Linear(
+                simple_env.observation_spec["observation"].shape[-1],
+                simple_env.action_spec.shape[-1],
+            ),
+            in_keys=["observation"],
+            out_keys=["action"],
+        )
+
+    def test_syncdatacollector_multiprocess_scheme(self, simple_policy):
+        scheme = MultiProcessWeightSyncScheme(strategy="state_dict")
+
+        collector = SyncDataCollector(
+            create_env_fn=lambda: GymEnv("CartPole-v1"),
+            policy=simple_policy,
+            frames_per_batch=64,
+            total_frames=128,
+            weight_sync_schemes={"policy": scheme},
+        )
+
+        new_weights = simple_policy.state_dict()
+        with torch.no_grad():
+            for key in new_weights:
+                new_weights[key].fill_(1.0)
+
+        collector.update_policy_weights_(new_weights)
+
+        for data in collector:
+            assert data.numel() > 0
+            break
+
+        collector.shutdown()
+
+    def test_multisyncdatacollector_multiprocess_scheme(self, simple_policy):
+        scheme = MultiProcessWeightSyncScheme(strategy="state_dict")
+
+        collector = MultiSyncDataCollector(
+            create_env_fn=[
+                lambda: GymEnv("CartPole-v1"),
+                lambda: GymEnv("CartPole-v1"),
+            ],
+            policy=simple_policy,
+            frames_per_batch=64,
+            total_frames=128,
+            weight_sync_schemes={"policy": scheme},
+        )
+
+        new_weights = simple_policy.state_dict()
+        with torch.no_grad():
+            for key in new_weights:
+                new_weights[key].fill_(1.0)
+
+        collector.update_policy_weights_(new_weights)
+
+        for data in collector:
+            assert data.numel() > 0
+            break
+
+        collector.shutdown()
+
+    def test_multisyncdatacollector_shared_mem_scheme(self, simple_policy):
+        scheme = SharedMemWeightSyncScheme(strategy="tensordict", auto_register=True)
+
+        collector = MultiSyncDataCollector(
+            create_env_fn=[
+                lambda: GymEnv("CartPole-v1"),
+                lambda: GymEnv("CartPole-v1"),
+            ],
+            policy=simple_policy,
+            frames_per_batch=64,
+            total_frames=128,
+            weight_sync_schemes={"policy": scheme},
+        )
+
+        new_weights = TensorDict.from_module(simple_policy)
+        with torch.no_grad():
+            new_weights["module"]["weight"].fill_(1.0)
+            new_weights["module"]["bias"].fill_(1.0)
+
+        collector.update_policy_weights_(new_weights)
+
+        for data in collector:
+            assert data.numel() > 0
+            break
+
+        collector.shutdown()
+
+    def test_collector_no_weight_sync(self, simple_policy):
+        scheme = NoWeightSyncScheme()
+
+        collector = SyncDataCollector(
+            create_env_fn=lambda: GymEnv("CartPole-v1"),
+            policy=simple_policy,
+            frames_per_batch=64,
+            total_frames=128,
+            weight_sync_schemes={"policy": scheme},
+        )
+
+        for data in collector:
+            assert data.numel() > 0
+            break
+
+        collector.shutdown()
+
+
+class TestMultiModelUpdates:
+    def test_multi_model_state_dict_updates(self):
+        env = GymEnv("CartPole-v1")
+
+        policy = TensorDictModule(
+            nn.Linear(
+                env.observation_spec["observation"].shape[-1], env.action_spec.shape[-1]
+            ),
+            in_keys=["observation"],
+            out_keys=["action"],
+        )
+
+        value = TensorDictModule(
+            nn.Linear(env.observation_spec["observation"].shape[-1], 1),
+            in_keys=["observation"],
+            out_keys=["value"],
+        )
+
+        weight_sync_schemes = {
+            "policy": MultiProcessWeightSyncScheme(strategy="state_dict"),
+            "value": MultiProcessWeightSyncScheme(strategy="state_dict"),
+        }
+
+        collector = SyncDataCollector(
+            create_env_fn=lambda: GymEnv("CartPole-v1"),
+            policy=policy,
+            frames_per_batch=64,
+            total_frames=128,
+            weight_sync_schemes=weight_sync_schemes,
+        )
+
+        policy_weights = policy.state_dict()
+        value_weights = value.state_dict()
+
+        with torch.no_grad():
+            for key in policy_weights:
+                policy_weights[key].fill_(1.0)
+            for key in value_weights:
+                value_weights[key].fill_(2.0)
+
+        collector.update_policy_weights_(
+            weights_dict={
+                "policy": policy_weights,
+                "value": value_weights,
+            }
+        )
+
+        for data in collector:
+            assert data.numel() > 0
+            break
+
+        collector.shutdown()
+        env.close()
+
+    def test_multi_model_tensordict_updates(self):
+        env = GymEnv("CartPole-v1")
+
+        policy = TensorDictModule(
+            nn.Linear(
+                env.observation_spec["observation"].shape[-1], env.action_spec.shape[-1]
+            ),
+            in_keys=["observation"],
+            out_keys=["action"],
+        )
+
+        value = TensorDictModule(
+            nn.Linear(env.observation_spec["observation"].shape[-1], 1),
+            in_keys=["observation"],
+            out_keys=["value"],
+        )
+
+        weight_sync_schemes = {
+            "policy": MultiProcessWeightSyncScheme(strategy="tensordict"),
+            "value": MultiProcessWeightSyncScheme(strategy="tensordict"),
+        }
+
+        collector = SyncDataCollector(
+            create_env_fn=lambda: GymEnv("CartPole-v1"),
+            policy=policy,
+            frames_per_batch=64,
+            total_frames=128,
+            weight_sync_schemes=weight_sync_schemes,
+        )
+
+        policy_weights = TensorDict.from_module(policy)
+        value_weights = TensorDict.from_module(value)
+
+        with torch.no_grad():
+            policy_weights["module"]["weight"].fill_(1.0)
+            policy_weights["module"]["bias"].fill_(1.0)
+            value_weights["module"]["weight"].fill_(2.0)
+            value_weights["module"]["bias"].fill_(2.0)
+
+        collector.update_policy_weights_(
+            weights_dict={
+                "policy": policy_weights,
+                "value": value_weights,
+            }
+        )
+
+        for data in collector:
+            assert data.numel() > 0
+            break
+
+        collector.shutdown()
+        env.close()
+
+
 class TestHelpers:
     def test_resolve_model_simple(self):
         class Context:
diff --git a/torchrl/collectors/collectors.py b/torchrl/collectors/collectors.py
index 34937c22d47..355e6e98db0 100644
--- a/torchrl/collectors/collectors.py
+++ b/torchrl/collectors/collectors.py
@@ -51,11 +51,7 @@
     VERBOSE,
 )
 from torchrl.collectors.utils import split_trajectories
-from torchrl.collectors.weight_update import (
-    MultiProcessedWeightUpdater,
-    VanillaWeightUpdater,
-    WeightUpdaterBase,
-)
+from torchrl.collectors.weight_update import WeightUpdaterBase
 from torchrl.data import ReplayBuffer
 from torchrl.data.utils import CloudpickleWrapper, DEVICE_TYPING
 from torchrl.envs.common import _do_nothing, EnvBase
@@ -70,6 +66,13 @@
     RandomPolicy,
     set_exploration_type,
 )
+from torchrl.weight_update.weight_sync_schemes import (
+    _resolve_model,
+    MultiProcessWeightSyncScheme,
+    WeightReceiver,
+    WeightSender,
+    WeightSyncScheme,
+)
 
 try:
     from torch.compiler import cudagraph_mark_step_begin
@@ -157,6 +160,9 @@ class DataCollectorBase(IterableDataset, metaclass=abc.ABCMeta):
     compiled_policy: bool
     cudagraphed_policy: bool
     _weight_updater: WeightUpdaterBase | None = None
+    _weight_sync_schemes: dict[str, WeightSyncScheme] | None = None
+    _weight_senders: dict[str, WeightSender] | None = None
+    _weight_receivers: dict[str, WeightReceiver] | None = None
     verbose: bool = False
 
     @property
@@ -197,15 +203,6 @@ def _get_policy_and_device(
         if policy_device is NO_DEFAULT:
             policy_device = self.policy_device
 
-        if not self.trust_policy:
-            env = getattr(self, "env", None)
-            policy = _make_compatible_policy(
-                policy,
-                getattr(env, "observation_spec", None),
-                env=env,
-                env_maker=env_maker,
-                env_maker_kwargs=env_maker_kwargs,
-            )
         if not policy_device:
             return policy, None
 
@@ -294,11 +291,59 @@ def async_shutdown(
         """
         return self.shutdown(timeout=timeout, close_env=close_env)
 
+    def _extract_weights_if_needed(self, weights: Any, model_id: str) -> Any:
+        """Extract weights from a model if needed.
+
+        Args:
+            weights: Either already-extracted weights or a model to extract from.
+            model_id: The model identifier for resolving string paths.
+
+        Returns:
+            Extracted weights in the appropriate format.
+        """
+        scheme = (
+            self._weight_sync_schemes.get(model_id)
+            if self._weight_sync_schemes
+            else None
+        )
+
+        if weights is None:
+            if model_id == "policy" and hasattr(self, "policy_weights"):
+                return self.policy_weights
+            elif model_id == "policy" and hasattr(self, "_policy_weights_dict"):
+                policy_device = (
+                    self.policy_device
+                    if not isinstance(self.policy_device, (list, tuple))
+                    else self.policy_device[0]
+                )
+                return self._policy_weights_dict.get(policy_device)
+            return None
+
+        if scheme is None:
+            return weights
+
+        from torchrl.weight_update.weight_sync_schemes import (
+            _resolve_model,
+            WeightStrategy,
+        )
+
+        strategy = WeightStrategy(extract_as=scheme.strategy)
+
+        if isinstance(weights, nn.Module):
+            return strategy.extract_weights(weights)
+        elif isinstance(weights, str):
+            model = _resolve_model(self, weights)
+            return strategy.extract_weights(model)
+        else:
+            return weights
+
     def update_policy_weights_(
         self,
         policy_or_weights: TensorDictBase | TensorDictModuleBase | dict | None = None,
         *,
         worker_ids: int | list[int] | torch.device | list[torch.device] | None = None,
+        model_id: str | None = None,
+        weights_dict: dict[str, Any] | None = None,
         **kwargs,
     ) -> None:
         """Updates the policy weights for the data collector, accommodating both local and remote execution contexts.
@@ -317,9 +362,15 @@ def update_policy_weights_(
             worker_ids (int | List[int] | torch.device | List[torch.device] | None, optional): Identifiers for the
                 workers that need to be updated. This is relevant when the collector has more than one worker associated
                 with it.
+            model_id (str | None, optional): The model identifier to update. If provided, only updates this specific
+                model. Cannot be used together with weights_dict.
+            weights_dict (dict[str, Any] | None, optional): Dictionary mapping model_id to weights for updating
+                multiple models atomically. Keys should match the model_ids registered in weight_sync_schemes.
+                Cannot be used together with model_id or policy_or_weights.
 
         Raises:
             TypeError: If `worker_ids` is provided but no `weight_updater` is configured.
+            ValueError: If conflicting parameters are provided (e.g., both model_id and weights_dict).
 
         .. note:: Users should extend the `WeightUpdaterBase` classes to customize
             the weight update logic for specific use cases. This method should not be overwritten.
@@ -335,9 +386,83 @@ def update_policy_weights_(
             )
             policy_or_weights = kwargs.pop("policy_weights")
 
-        self.weight_updater(
-            policy_or_weights=policy_or_weights, worker_ids=worker_ids, **kwargs
-        )
+        if weights_dict is not None and model_id is not None:
+            raise ValueError("Cannot specify both 'weights_dict' and 'model_id'")
+
+        if weights_dict is not None and policy_or_weights is not None:
+            raise ValueError(
+                "Cannot specify both 'weights_dict' and 'policy_or_weights'"
+            )
+
+        # Priority: new weight sync schemes > old weight updater system
+        if self._weight_senders:
+            if weights_dict is not None:
+                for target_model_id, weights in weights_dict.items():
+                    if target_model_id not in self._weight_senders:
+                        raise KeyError(
+                            f"Model '{target_model_id}' not found in registered weight senders. "
+                            f"Available models: {list(self._weight_senders.keys())}"
+                        )
+                    processed_weights = self._extract_weights_if_needed(
+                        weights, target_model_id
+                    )
+                    self._weight_senders[target_model_id].update_weights(
+                        processed_weights
+                    )
+            elif model_id is not None:
+                if model_id not in self._weight_senders:
+                    raise KeyError(
+                        f"Model '{model_id}' not found in registered weight senders. "
+                        f"Available models: {list(self._weight_senders.keys())}"
+                    )
+                processed_weights = self._extract_weights_if_needed(
+                    policy_or_weights, model_id
+                )
+                self._weight_senders[model_id].update_weights(processed_weights)
+            else:
+                if "policy" in self._weight_senders:
+                    processed_weights = self._extract_weights_if_needed(
+                        policy_or_weights, "policy"
+                    )
+                    self._weight_senders["policy"].update_weights(processed_weights)
+                elif len(self._weight_senders) == 1:
+                    single_model_id = next(iter(self._weight_senders.keys()))
+                    single_sender = self._weight_senders[single_model_id]
+                    processed_weights = self._extract_weights_if_needed(
+                        policy_or_weights, single_model_id
+                    )
+                    single_sender.update_weights(processed_weights)
+                else:
+                    for target_model_id, sender in self._weight_senders.items():
+                        processed_weights = self._extract_weights_if_needed(
+                            policy_or_weights, target_model_id
+                        )
+                        sender.update_weights(processed_weights)
+
+        elif self._weight_updater is not None:
+            # Fall back to old weight updater system
+            self.weight_updater(
+                policy_or_weights=policy_or_weights, worker_ids=worker_ids, **kwargs
+            )
+        else:
+            # No weight updater configured
+            # For single-process collectors, apply weights locally if explicitly provided
+            if policy_or_weights is not None:
+                from torchrl.weight_update.weight_sync_schemes import WeightStrategy
+
+                # Use WeightStrategy to apply weights properly
+                strategy = WeightStrategy(extract_as="tensordict")
+
+                # Extract weights if needed
+                if isinstance(policy_or_weights, nn.Module):
+                    weights = strategy.extract_weights(policy_or_weights)
+                else:
+                    weights = policy_or_weights
+
+                # Apply to local policy
+                if hasattr(self, "policy") and isinstance(self.policy, nn.Module):
+                    strategy.apply_weights(self.policy, weights)
+            # Otherwise, no action needed - policy is local and changes are immediately visible
 
     def __iter__(self) -> Iterator[TensorDictBase]:
         try:
@@ -547,14 +672,19 @@ class SyncDataCollector(DataCollectorBase):
             but populate the buffer instead.
             Defaults to ``None``.
 
-            .. seealso:: By default, the buffer is populated every time a (batch of) frames is collected.
-                If the buffer needs to be extended with entire rollouts, set `extend_buffer` to `True`.
+            .. seealso:: By default (``extend_buffer=True``), the buffer is extended with entire rollouts.
+                If the buffer needs to be populated with individual frames as they are collected,
+                set ``extend_buffer=False`` (deprecated).
 
-            .. warning:: Using a replay buffer with a `postproc` or `split_trajs=True` is prohibited unless
+            .. warning:: Using a replay buffer with a `postproc` or `split_trajs=True` requires
                 `extend_buffer=True`, as the whole batch needs to be observed to apply these transforms.
 
         extend_buffer (bool, optional): if `True`, the replay buffer is extended with entire rollouts and not
-            with single steps. Defaults to `False`.
+            with single steps. Defaults to `True`.
+
+            .. note:: Setting this to `False` is deprecated and will be removed in a future version.
+                Extending the buffer with entire rollouts is the recommended approach for better
+                compatibility with postprocessing and trajectory splitting.
         trust_policy (bool, optional): if ``True``, a non-TensorDictModule policy will be trusted to be
             assumed to be compatible with the collector. This defaults to ``True`` for CudaGraphModules
             and ``False`` otherwise.
@@ -636,6 +766,8 @@ class SyncDataCollector(DataCollectorBase):
 
     """
 
+    _ignore_rb: bool = False
+
     def __init__(
         self,
         create_env_fn: (
@@ -664,7 +796,8 @@ def __init__(
         set_truncated: bool = False,
         use_buffers: bool | None = None,
         replay_buffer: ReplayBuffer | None = None,
-        extend_buffer: bool = False,
+        extend_buffer: bool = True,
+        local_init_rb: bool | None = None,
         trust_policy: bool | None = None,
         compile_policy: bool | dict[str, Any] | None = None,
         cudagraph_policy: bool | dict[str, Any] | None = None,
@@ -672,6 +805,7 @@ def __init__(
         weight_updater: WeightUpdaterBase
         | Callable[[], WeightUpdaterBase]
         | None = None,
+        weight_sync_schemes: dict[str, WeightSyncScheme] | None = None,
         track_policy_version: bool = False,
         **kwargs,
     ):
@@ -794,33 +928,38 @@ def __init__(
 
         # Policy version tracking setup
         self.policy_version_tracker = track_policy_version
-        if PolicyVersion is not None:
-            if isinstance(track_policy_version, bool) and track_policy_version:
-                from torchrl.envs.batched_envs import BatchedEnvBase
+        if isinstance(track_policy_version, bool) and track_policy_version:
+            from torchrl.envs.batched_envs import BatchedEnvBase
 
-                if isinstance(self.env, BatchedEnvBase):
-                    raise RuntimeError(
-                        "BatchedEnvBase is not supported for policy version tracking. Please add the PolicyVersion transform to the environment manually, "
-                        "and pass that transform to the collector."
-                    )
-                self.policy_version_tracker = PolicyVersion()
-                self.env = self.env.append_transform(self.policy_version_tracker)  # type: ignore
-            elif hasattr(
-                track_policy_version, "increment_version"
-            ):  # Check if it's a PolicyVersion instance
-                self.policy_version_tracker = track_policy_version
-                self.env = self.env.append_transform(self.policy_version_tracker)  # type: ignore
-            else:
-                self.policy_version_tracker = None
-        else:
-            if track_policy_version:
-                raise ImportError(
-                    "PolicyVersion is not available. Please install the LLM dependencies or set track_policy_version=False."
+            if isinstance(self.env, BatchedEnvBase):
+                raise RuntimeError(
+                    "BatchedEnvBase is not supported for policy version tracking. Please add the PolicyVersion transform to the environment manually, "
+                    "and pass that transform to the collector."
                 )
+            self.policy_version_tracker = PolicyVersion()
+            self.env = self.env.append_transform(self.policy_version_tracker)  # type: ignore
+        elif hasattr(
+            track_policy_version, "increment_version"
+        ):  # Check if it's a PolicyVersion instance
+            self.policy_version_tracker = track_policy_version
+            self.env = self.env.append_transform(self.policy_version_tracker)  # type: ignore
+        else:
             self.policy_version_tracker = None
         self.replay_buffer = replay_buffer
         self.extend_buffer = extend_buffer
-        if self.replay_buffer is not None:
+
+        # Handle local_init_rb deprecation for SyncDataCollector
+        if local_init_rb is None:
+            local_init_rb = False  # Default for SyncDataCollector
+            if replay_buffer is not None and not local_init_rb:
+                warnings.warn(
+                    "local_init_rb=False is deprecated and will be removed in v0.12. "
+                    "The new storage-level initialization provides better performance.",
+                    FutureWarning,
+                )
+        self.local_init_rb = local_init_rb
+
+        if self.replay_buffer is not None and not self._ignore_rb:
             if postproc is not None and not self.extend_buffer:
                 raise TypeError(
                     "postproc must be None when a replay buffer is passed, or extend_buffer must be set to True."
@@ -852,23 +991,37 @@ def __init__(
         if hasattr(self.env, "register_collector"):
             self.env.register_collector(self)
 
-        (self.policy, self.get_weights_fn,) = self._get_policy_and_device(
+        self._original_policy = policy
+        (policy, self.get_weights_fn,) = self._get_policy_and_device(
             policy=policy,
         )
-        if isinstance(self.policy, nn.Module):
+
+        if not self.trust_policy:
+            self.policy = policy
+            env = getattr(self, "env", None)
+            wrapped_policy = _make_compatible_policy(
+                policy=policy,
+                observation_spec=getattr(env, "observation_spec", None),
+                env=self.env,
+            )
+            self._wrapped_policy = wrapped_policy
+        else:
+            self.policy = self._wrapped_policy = policy
+
+        if isinstance(self._wrapped_policy, nn.Module):
             self.policy_weights = TensorDict.from_module(
-                self.policy, as_module=True
+                self._wrapped_policy, as_module=True
             ).data
         else:
             self.policy_weights = TensorDict()
 
         if self.compiled_policy:
-            self.policy = compile_with_warmup(
-                self.policy, **self.compiled_policy_kwargs
+            self._wrapped_policy = compile_with_warmup(
+                self._wrapped_policy, **self.compiled_policy_kwargs
             )
         if self.cudagraphed_policy:
-            self.policy = CudaGraphModule(
-                self.policy,
+            self._wrapped_policy = CudaGraphModule(
+                self._wrapped_policy,
                 in_keys=[],
                 out_keys=[],
                 device=self.policy_device,
@@ -975,16 +1128,44 @@ def __init__(
         self._frames = 0
         self._iter = -1
 
-        if weight_updater is None:
-            weight_updater = VanillaWeightUpdater(
-                weight_getter=self.get_weights_fn, policy_weights=self.policy_weights
-            )
-        elif not isinstance(weight_updater, WeightUpdaterBase):
-            raise TypeError(
-                f"weight_updater must be a subclass of WeightUpdaterBase. Got {type(weight_updater)} instead."
-            )
+        # Set up weight synchronization - prefer new schemes over legacy updater
+        # For single-process SyncDataCollector, no weight sync is needed (policy is local)
+        # Weight sync schemes are only needed for multi-process/distributed collectors
+        if weight_sync_schemes is not None:
+            # Use new simplified weight synchronization system
+            self._weight_sync_schemes = weight_sync_schemes
+            self._weight_senders = {}
+
+            # For single-process collectors, we don't need senders/receivers
+            # The policy is local and changes are immediately visible
+            # Senders will be set up in multiprocess collectors during _run_processes
+
+            self.weight_updater = None  # Don't use legacy system
+        elif weight_updater is not None:
+            # Use legacy weight updater system if explicitly provided
+            if not isinstance(weight_updater, WeightUpdaterBase):
+                if callable(weight_updater):
+                    weight_updater = weight_updater()
+                else:
+                    raise TypeError(
+                        f"weight_updater must be a subclass of WeightUpdaterBase. Got {type(weight_updater)} instead."
+                    )
 
-        self.weight_updater = weight_updater
+            warnings.warn(
+                "Using WeightUpdaterBase is deprecated. Please use weight_sync_schemes instead. "
+                "This will be removed in a future version.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            self.weight_updater = weight_updater
+            self._weight_sync_schemes = None
+            self._weight_senders = {}
+        else:
+            # No weight sync needed for single-process collectors
+            # The policy is local and changes are immediately visible
+            self.weight_updater = None
+            self._weight_sync_schemes = None
+            self._weight_senders = {}
 
     @property
     def _traj_pool(self):
@@ -1034,18 +1215,18 @@ def _maybe_make_final_rollout(self, make_rollout: bool):
         self._policy_output_keys = set()
         if (
             make_rollout
-            and hasattr(self.policy, "spec")
-            and self.policy.spec is not None
-            and all(v is not None for v in self.policy.spec.values(True, True))
+            and hasattr(self._wrapped_policy, "spec")
+            and self._wrapped_policy.spec is not None
+            and all(v is not None for v in self._wrapped_policy.spec.values(True, True))
         ):
             if any(
                 key not in self._final_rollout.keys(isinstance(key, tuple))
-                for key in self.policy.spec.keys(True, True)
+                for key in self._wrapped_policy.spec.keys(True, True)
             ):
                 # if policy spec is non-empty, all the values are not None and the keys
                 # match the out_keys we assume the user has given all relevant information
                 # the policy could have more keys than the env:
-                policy_spec = self.policy.spec
+                policy_spec = self._wrapped_policy.spec
                 if policy_spec.ndim < self._final_rollout.ndim:
                     policy_spec = policy_spec.expand(self._final_rollout.shape)
                 for key, spec in policy_spec.items(True, True):
@@ -1055,10 +1236,10 @@ def _maybe_make_final_rollout(self, make_rollout: bool):
                     self._final_rollout.set(key, spec.zero())
         elif (
             not make_rollout
-            and hasattr(self.policy, "out_keys")
-            and self.policy.out_keys
+            and hasattr(self._wrapped_policy, "out_keys")
+            and self._wrapped_policy.out_keys
         ):
-            self._policy_output_keys = list(self.policy.out_keys)
+            self._policy_output_keys = list(self._wrapped_policy.out_keys)
         else:
             if make_rollout:
                 # otherwise, we perform a small number of steps with the policy to
@@ -1078,7 +1259,7 @@ def _maybe_make_final_rollout(self, make_rollout: bool):
                 )  # to test if values have changed in-place
                 if self.compiled_policy:
                     cudagraph_mark_step_begin()
-                policy_output = self.policy(policy_input)
+                policy_output = self._wrapped_policy(policy_input)
 
                 # check that we don't have exclusive keys, because they don't appear in keys
                 def check_exclusive(val):
@@ -1319,7 +1500,7 @@ def cuda_check(tensor: torch.Tensor):
                             event.record()
                             event.synchronize()
                     yield tensordict_out
-                elif self.replay_buffer is not None:
+                elif self.replay_buffer is not None and not self._ignore_rb:
                     self.replay_buffer.extend(tensordict_out)
                     if self.verbose:
                         torchrl_logger.info(
@@ -1539,7 +1720,7 @@ def rollout(self) -> TensorDictBase:
                     # we still do the assignment for security
                     if self.compiled_policy:
                         cudagraph_mark_step_begin()
-                    policy_output = self.policy(policy_input)
+                    policy_output = self._wrapped_policy(policy_input)
                     if self.compiled_policy:
                         policy_output = policy_output.clone()
                     if self._shuttle is not policy_output:
@@ -1575,7 +1756,11 @@ def rollout(self) -> TensorDictBase:
                         next_data.clear_device_()
                     self._shuttle.set("next", next_data)
 
-                if self.replay_buffer is not None and not self.extend_buffer:
+                if (
+                    self.replay_buffer is not None
+                    and not self._ignore_rb
+                    and not self.extend_buffer
+                ):
                     self.replay_buffer.add(self._shuttle)
                     if self._increment_frames(self._shuttle.numel()):
                         return
@@ -1606,7 +1791,11 @@ def rollout(self) -> TensorDictBase:
                     self.interruptor is not None
                     and self.interruptor.collection_stopped()
                 ):
-                    if self.replay_buffer is not None and not self.extend_buffer:
+                    if (
+                        self.replay_buffer is not None
+                        and not self._ignore_rb
+                        and not self.extend_buffer
+                    ):
                         return
                     result = self._final_rollout
                     if self._use_buffers:
@@ -1643,7 +1832,11 @@ def rollout(self) -> TensorDictBase:
                                 self._final_rollout.ndim - 1,
                                 out=self._final_rollout,
                             )
-                elif self.replay_buffer is not None and not self.extend_buffer:
+                elif (
+                    self.replay_buffer is not None
+                    and not self._ignore_rb
+                    and not self.extend_buffer
+                ):
                     return
                 else:
                     result = TensorDict.maybe_dense_stack(tensordicts, dim=-1)
@@ -1775,7 +1968,7 @@ def load_state_dict(self, state_dict: OrderedDict, **kwargs) -> None:
     def __repr__(self) -> str:
         try:
             env_str = indent(f"env={self.env}", 4 * " ")
-            policy_str = indent(f"policy={self.policy}", 4 * " ")
+            policy_str = indent(f"policy={self._wrapped_policy}", 4 * " ")
             td_out_str = indent(
                 f"td_out={getattr(self, '_final_rollout', None)}", 4 * " "
             )
@@ -1820,7 +2013,7 @@ def get_policy_version(self) -> str | int | None:
     def getattr_policy(self, attr):
         """Get an attribute from the policy."""
         # send command to policy to return the attr
-        return getattr(self.policy, attr)
+        return getattr(self._wrapped_policy, attr)
 
     def getattr_env(self, attr):
         """Get an attribute from the environment."""
@@ -2002,6 +2195,11 @@ class _MultiDataCollector(DataCollectorBase):
             but populate the buffer instead. Defaults to ``None``.
         extend_buffer (bool, optional): if `True`, the replay buffer is extended with entire rollouts and not
             with single steps. Defaults to `True` for multiprocessed data collectors.
+        local_init_rb (bool, optional): if ``False``, the collector will use fake data to initialize
+            the replay buffer in the main process (legacy behavior). If ``True``, the storage-level
+            coordination will handle initialization with real data from worker processes.
+            Defaults to ``None``, which maintains backward compatibility but shows a deprecation warning.
+            This parameter is deprecated and will be removed in v0.12.
         trust_policy (bool, optional): if ``True``, a non-TensorDictModule policy will be trusted to be
             assumed to be compatible with the collector. This defaults to ``True`` for CudaGraphModules
             and ``False`` otherwise.
@@ -2021,6 +2219,8 @@ class _MultiDataCollector(DataCollectorBase):
             If not provided, a :class:`~torchrl.collectors.MultiProcessedWeightUpdater` will be used by default,
             which handles weight synchronization across multiple processes.
             Consider using a constructor if the updater needs to be serialized.
+        weight_sync_schemes (dict[str, WeightSyncScheme], optional): A dictionary of weight sync schemes for the different models.
+            If not provided, a :class:`~torchrl.collectors.MultiProcessWeightSyncScheme` will be used by default.
         track_policy_version (bool or PolicyVersion, optional): if ``True``, the collector will track the version of the policy.
             This will be mediated by the :class:`~torchrl.envs.llm.transforms.policy_version.PolicyVersion` transform, which will be added to the environment.
             Alternatively, a :class:`~torchrl.envs.llm.transforms.policy_version.PolicyVersion` instance can be passed, which will be used to track
@@ -2064,6 +2264,7 @@ def __init__(
         replay_buffer: ReplayBuffer | None = None,
         extend_buffer: bool = True,
         replay_buffer_chunk: bool | None = None,
+        local_init_rb: bool | None = None,
         trust_policy: bool | None = None,
         compile_policy: bool | dict[str, Any] | None = None,
         cudagraph_policy: bool | dict[str, Any] | None = None,
@@ -2071,6 +2272,7 @@ def __init__(
         weight_updater: WeightUpdaterBase
         | Callable[[], WeightUpdaterBase]
         | None = None,
+        weight_sync_schemes: dict[str, WeightSyncScheme] | None = None,
         track_policy_version: bool = False,
     ):
         self.closed = True
@@ -2142,6 +2344,21 @@ def __init__(
 
         self._use_buffers = use_buffers
         self.replay_buffer = replay_buffer
+
+        # Handle local_init_rb deprecation
+        if local_init_rb is None:
+            # v0.11: Default to False (current behavior), show deprecation warning
+            # v0.12: Default to True (new behavior)
+            local_init_rb = False  # Will become True in 0.12
+            if replay_buffer is not None and not local_init_rb:
+                warnings.warn(
+                    "local_init_rb=False is deprecated and will be removed in v0.12. "
+                    "The new storage-level initialization provides better performance.",
+                    FutureWarning,
+                )
+
+        self.local_init_rb = local_init_rb
+
         self._check_replay_buffer_init()
         if replay_buffer_chunk is not None:
             if extend_buffer is None:
@@ -2186,17 +2403,20 @@ def __init__(
                 if type(policy_new_device) is not type(policy):
                     policy = policy_new_device
                 weights = (
-                    TensorDict.from_module(policy_new_device).data
+                    TensorDict.from_module(policy_new_device)
                     if isinstance(policy_new_device, nn.Module)
                     else TensorDict()
                 )
                 self._policy_weights_dict[policy_device] = weights
             self._get_weights_fn = get_weights_fn
             if weight_updater is None:
-                weight_updater = MultiProcessedWeightUpdater(
-                    get_server_weights=self._get_weights_fn,
-                    policy_weights=self._policy_weights_dict,
-                )
+                # For multiprocessed collectors, use MultiProcessWeightSyncScheme by default
+                if weight_sync_schemes is None:
+                    weight_sync_schemes = {"policy": MultiProcessWeightSyncScheme()}
+                # Don't create legacy weight updater if we have schemes
+            else:
+                # Legacy weight updater was explicitly provided
+                pass
         elif weight_updater is None:
             warnings.warn(
                 "weight_updater is None, but policy_factory is provided. This means that the server will "
@@ -2207,7 +2427,18 @@ def __init__(
                 "This will work whenever your inference and training policies are nn.Module instances with similar structures."
             )
 
-        self.weight_updater = weight_updater
+        # Set up weight synchronization - prefer new schemes over legacy updater
+        if weight_sync_schemes is not None:
+            # Use new simplified weight synchronization system
+            self._weight_sync_schemes = weight_sync_schemes
+            self._weight_senders = {}
+            # Senders will be created in _run_processes when pipes are available
+            self.weight_updater = None  # Don't use legacy system
+        else:
+            # Fall back to legacy weight updater system
+            self.weight_updater = weight_updater
+            self._weight_sync_schemes = None
+            self._weight_senders = {}
 
         # Policy version tracking setup
         self.policy_version_tracker = track_policy_version
@@ -2301,8 +2532,17 @@ def __init__(
     def _check_replay_buffer_init(self):
         if self.replay_buffer is None:
             return
-        is_init = getattr(self.replay_buffer._storage, "initialized", True)
+        is_init = hasattr(self.replay_buffer, "_storage") and getattr(
+            self.replay_buffer._storage, "initialized", True
+        )
         if not is_init:
+            if self.local_init_rb:
+                # New behavior: storage handles all coordination itself
+                # Nothing to do here - the storage will coordinate during first write
+                self.replay_buffer.share()
+                return
+
+            # Legacy behavior: fake tensordict initialization
             if isinstance(self.create_env_fn[0], EnvCreator):
                 fake_td = self.create_env_fn[0].meta_data.tensordict
             elif isinstance(self.create_env_fn[0], EnvBase):
@@ -2394,6 +2634,15 @@ def _run_processes(self) -> None:
                 1, torch.get_num_threads() - total_workers
             )  # 1 more thread for this proc
 
+        # Initialize weight senders for multiprocess collectors
+        if self._weight_sync_schemes:
+            # Create one sender per model using scheme's factory method
+            for model_id, scheme in self._weight_sync_schemes.items():
+                sender = scheme.create_sender()
+                sender._model_id = model_id
+                if hasattr(sender, "set_context"):
+                    sender.set_context(self, model_id)
+                self._weight_senders[model_id] = sender
         torch.set_num_threads(self.num_threads)
         queue_out = mp.Queue(self._queue_len)  # sends data from proc to main
         self.procs = []
@@ -2465,6 +2714,7 @@ def _run_processes(self) -> None:
                     "postproc": self.postprocs
                     if self.replay_buffer is not None
                     else None,
+                    "weight_sync_schemes": self._weight_sync_schemes,
                 }
                 proc = _ProcessNoWarn(
                     target=_main_async_collector,
@@ -2474,6 +2724,21 @@ def _run_processes(self) -> None:
                 # proc.daemon can't be set as daemonic processes may be launched by the process itself
                 try:
                     proc.start()
+                except TypeError as err:
+                    if "cannot pickle" in str(err):
+                        raise RuntimeError(
+                            "A non-serializable object was passed to the collector workers."
+                        ) from err
+                except RuntimeError as err:
+                    if "Cowardly refusing to serialize non-leaf tensor" in str(err):
+                        raise RuntimeError(
+                            "At least one of the tensors in the policy, replay buffer, environment constructor or postprocessor requires gradients. "
+                            "This is not supported in multiprocessed data collectors.\n- For ReplayBuffer transforms, use a `transform_factory` instead with `delayed_init=True`.\n"
+                            "- Make sure your environment constructor does not reference tensors already instantiated on the main process.\n"
+                            "- Since no gradient can be propagated through the Collector pipes, the backward graph is never needed. Consider using detached tensors instead."
+                        ) from err
+                    else:
+                        raise err
                 except _pickle.PicklingError as err:
                     if "<lambda>" in str(err):
                         raise RuntimeError(
@@ -2488,11 +2753,62 @@ def _run_processes(self) -> None:
                 pipe_child.close()
                 self.procs.append(proc)
                 self.pipes.append(pipe_parent)
-        for pipe_parent in self.pipes:
+
+                # Register worker with senders
+                if self._weight_senders:
+                    for _, sender in self._weight_senders.items():
+                        sender.register_worker(i, pipe_parent)
+
+        for i, pipe_parent in enumerate(self.pipes):
             pipe_parent.poll(timeout=INSTANTIATE_TIMEOUT)
-            msg = pipe_parent.recv()
+            try:
+                msg = pipe_parent.recv()
+            except EOFError as e:
+                raise RuntimeError(
+                    f"Worker {i} failed to initialize and closed the connection before sending status. "
+                    f"This typically indicates that the worker process crashed during initialization. "
+                    f"Check the worker process logs for the actual error."
+                ) from e
             if msg != "instantiated":
-                raise RuntimeError(msg)
+                # Check if it's an error dict from worker
+                if isinstance(msg, dict) and msg.get("error"):
+                    # Reconstruct the exception from the worker
+                    exc_type_name = msg["exception_type"]
+                    exc_msg = msg["exception_msg"]
+                    traceback_str = msg["traceback"]
+
+                    # Try to get the actual exception class
+                    exc_class = None
+                    exc_module = msg["exception_module"]
+
+                    if exc_module == "builtins":
+                        # Get from builtins
+                        import builtins
+
+                        exc_class = getattr(builtins, exc_type_name, None)
+                    else:
+                        # Try to import from the module
+                        try:
+                            import importlib
+
+                            mod = importlib.import_module(exc_module)
+                            exc_class = getattr(mod, exc_type_name, None)
+                        except Exception:
+                            pass
+
+                    # Re-raise with original exception type if possible
+                    if exc_class is not None:
+                        raise exc_class(
+                            f"{exc_msg}\n\nWorker traceback:\n{traceback_str}"
+                        )
+                    else:
+                        # Fall back to RuntimeError if we can't get the original type
+                        raise RuntimeError(
+                            f"Worker {i} raised {exc_type_name}: {exc_msg}\n\nWorker traceback:\n{traceback_str}"
+                        )
+                else:
+                    # Legacy string error message
+                    raise RuntimeError(msg)
         self.queue_out = queue_out
         self.closed = False
 
@@ -3057,6 +3373,7 @@ def iterator(self) -> Iterator[TensorDictBase]:
                     msg = "continue_random"
                 else:
                     msg = "continue"
+                # Debug: sending 'continue'
                 self.pipes[idx].send((None, msg))
 
             self._iter += 1
@@ -3120,13 +3437,13 @@ def iterator(self) -> Iterator[TensorDictBase]:
                     # mask buffers if cat, and create a mask if stack
                     if cat_results != "stack":
                         buffers = {}
-                        for idx, buffer in self.buffers.items():
+                        for worker_idx, buffer in self.buffers.items():
                             valid = buffer.get(("collector", "traj_ids")) != -1
                             if valid.ndim > 2:
                                 valid = valid.flatten(0, -2)
                             if valid.ndim == 2:
                                 valid = valid.any(0)
-                            buffers[idx] = buffer[..., valid]
+                            buffers[worker_idx] = buffer[..., valid]
                     else:
                         for buffer in self.buffers.values():
                             with buffer.unlock_():
@@ -3138,6 +3455,11 @@ def iterator(self) -> Iterator[TensorDictBase]:
                 else:
                     buffers = self.buffers
 
+                # Skip frame counting if this worker didn't send data this iteration
+                # (happens when reusing buffers or on first iteration with some workers)
+                if idx not in buffers:
+                    continue
+
                 workers_frames[idx] = workers_frames[idx] + buffers[idx].numel()
 
                 if workers_frames[idx] >= self.total_frames:
@@ -3156,7 +3478,7 @@ def iterator(self) -> Iterator[TensorDictBase]:
             # we have to correct the traj_ids to make sure that they don't overlap
             # We can count the number of frames collected for free in this loop
             n_collected = 0
-            for idx in range(self.num_workers):
+            for idx in buffers.keys():
                 buffer = buffers[idx]
                 traj_ids = buffer.get(("collector", "traj_ids"))
                 if preempt:
@@ -3775,6 +4097,7 @@ def _main_async_collector(
     policy_factory: Callable | None = None,
     collector_class: type | Callable[[], DataCollectorBase] | None = None,
     postproc: Callable[[TensorDictBase], TensorDictBase] | None = None,
+    weight_sync_schemes: dict[str, WeightSyncScheme] | None = None,
 ) -> None:
     if collector_class is None:
         collector_class = SyncDataCollector
@@ -3782,40 +4105,77 @@ def _main_async_collector(
     # init variables that will be cleared when closing
     collected_tensordict = data = next_data = data_in = inner_collector = dc_iter = None
 
-    inner_collector = collector_class(
-        create_env_fn,
-        create_env_kwargs=create_env_kwargs,
-        policy=policy,
-        policy_factory=policy_factory,
-        total_frames=-1,
-        max_frames_per_traj=max_frames_per_traj,
-        frames_per_batch=frames_per_batch,
-        reset_at_each_iter=reset_at_each_iter,
-        postproc=postproc,
-        split_trajs=False,
-        storing_device=storing_device,
-        policy_device=policy_device,
-        env_device=env_device,
-        exploration_type=exploration_type,
-        reset_when_done=reset_when_done,
-        return_same_td=replay_buffer is None,
-        interruptor=interruptor,
-        set_truncated=set_truncated,
-        use_buffers=use_buffers,
-        replay_buffer=replay_buffer if not extend_buffer else None,
-        extend_buffer=False,
-        traj_pool=traj_pool,
-        trust_policy=trust_policy,
-        compile_policy=compile_policy,
-        cudagraph_policy=cudagraph_policy,
-        no_cuda_sync=no_cuda_sync,
-    )
-    use_buffers = inner_collector._use_buffers
-    if verbose:
-        torchrl_logger.info("Sync data collector created")
-    dc_iter = iter(inner_collector)
-    j = 0
-    pipe_child.send("instantiated")
+    try:
+        collector_class._ignore_rb = extend_buffer
+        inner_collector = collector_class(
+            create_env_fn,
+            create_env_kwargs=create_env_kwargs,
+            policy=policy,
+            policy_factory=policy_factory,
+            total_frames=-1,
+            max_frames_per_traj=max_frames_per_traj,
+            frames_per_batch=frames_per_batch,
+            reset_at_each_iter=reset_at_each_iter,
+            postproc=postproc,
+            split_trajs=False,
+            storing_device=storing_device,
+            policy_device=policy_device,
+            env_device=env_device,
+            exploration_type=exploration_type,
+            reset_when_done=reset_when_done,
+            return_same_td=replay_buffer is None,
+            interruptor=interruptor,
+            set_truncated=set_truncated,
+            use_buffers=use_buffers,
+            replay_buffer=replay_buffer,
+            extend_buffer=False,
+            traj_pool=traj_pool,
+            trust_policy=trust_policy,
+            compile_policy=compile_policy,
+            cudagraph_policy=cudagraph_policy,
+            no_cuda_sync=no_cuda_sync,
+            weight_sync_schemes=weight_sync_schemes,
+        )
+
+        # Set up weight receivers for worker process
+        if weight_sync_schemes:
+            inner_collector._weight_receivers = {}
+            for model_id, scheme in weight_sync_schemes.items():
+                receiver = scheme.create_receiver()
+                receiver.set_context(inner_collector)
+                receiver.register_worker_transport(pipe_child)
+
+                model = _resolve_model(inner_collector, model_id)
+                receiver.register_model(model)
+
+                inner_collector._weight_receivers[model_id] = receiver
+        else:
+            inner_collector._weight_receivers = {}
+
+        use_buffers = inner_collector._use_buffers
+        if verbose:
+            torchrl_logger.info("Sync data collector created")
+        dc_iter = iter(inner_collector)
+        j = 0
+        pipe_child.send("instantiated")
+    except Exception as e:
+        # Send error information to main process
+        # We send a dict with the exception info so we can recreate it in the main process
+        import traceback
+
+        error_info = {
+            "error": True,
+            "exception_type": type(e).__name__,
+            "exception_module": type(e).__module__,
+            "exception_msg": str(e),
+            "traceback": traceback.format_exc(),
+        }
+        try:
+            pipe_child.send(error_info)
+        except Exception:
+            # If pipe is broken, nothing we can do
+            pass
+        return
 
     has_timed_out = False
     counter = 0
@@ -3888,7 +4248,81 @@ def _main_async_collector(
                 data_in = None
                 # TODO: this does not work with random frames
                 msg = "continue"
+        # Note: The "continue" message handling has been moved below after update_weights handling
+        # to allow falling through from update_weights to continue
+
+        if msg == "update":
+            torchrl_logger.info(f"worker {idx} updating the params...")
+            inner_collector.update_policy_weights_(policy_weights=data_in)
+            pipe_child.send((j, "updated"))
+            has_timed_out = False
+            continue
+
+        if msg == "register_shared_weights":
+            # Shared memory lazy registration: main process sends buffer reference
+            if verbose:
+                torchrl_logger.info(
+                    f"worker {idx} received shared memory buffer registration"
+                )
+            model_id, shared_buffer = data_in
+
+            # Store the shared buffer reference for this model
+            # The receiver will use this buffer for all future weight accesses
+            if (
+                inner_collector._weight_receivers
+                and model_id in inner_collector._weight_receivers
+            ):
+                # Update receiver's buffer reference
+                receiver = inner_collector._weight_receivers[model_id]
+                # Store the shared buffer - the model's parameters should point to this
+                if hasattr(receiver, "_shared_weights"):
+                    receiver._shared_weights[model_id] = shared_buffer
+
+                # Apply the buffer to the model immediately
+                receiver.apply_weights(shared_buffer)
+
+                if verbose:
+                    torchrl_logger.info(
+                        f"worker {idx} registered shared buffer for model '{model_id}'"
+                    )
+            else:
+                torchrl_logger.warning(
+                    f"worker {idx} received shared buffer for unknown model '{model_id}'"
+                )
+
+            # Send acknowledgment back to main process
+            pipe_child.send((None, "registered"))
+            has_timed_out = False
+            continue
+
+        if msg == "update_weights":
+            # New weight update protocol for simplified weight sync system
+            if verbose:
+                torchrl_logger.info(
+                    f"worker {idx} received weight update via new protocol"
+                )
+            model_id, weights = data_in
+
+            # Apply weights using the appropriate receiver for this model
+            if (
+                inner_collector._weight_receivers
+                and model_id in inner_collector._weight_receivers
+            ):
+                inner_collector._weight_receivers[model_id].apply_weights(weights)
+            else:
+                torchrl_logger.warning(
+                    f"worker {idx} received weights for unknown model '{model_id}'"
+                )
+
+            # After applying weights, we continue collecting immediately as if we received
+            # a "continue" message. This ensures the worker keeps collecting data without
+            # waiting for an explicit continue from the main process.
+            has_timed_out = False
+            msg = "continue"
+            # Now check if we should continue collecting
+
         if msg in ("continue", "continue_random"):
+            # This block handles both explicit continue messages and implicit ones after weight updates
             if msg == "continue_random":
                 inner_collector.init_random_frames = float("inf")
             else:
@@ -3980,14 +4414,7 @@ def cast_tensor(x, MPS_ERROR=MPS_ERROR):
                 has_timed_out = True
                 continue
 
-        elif msg == "update":
-            torchrl_logger.info(f"worker {idx} updating the params...")
-            inner_collector.update_policy_weights_(policy_weights=data_in)
-            pipe_child.send((j, "updated"))
-            has_timed_out = False
-            continue
-
-        elif msg == "seed":
+        if msg == "seed":
             data_in, static_seed = data_in
             new_seed = inner_collector.set_seed(data_in, static_seed=static_seed)
             torch.manual_seed(data_in)
diff --git a/torchrl/collectors/distributed/generic.py b/torchrl/collectors/distributed/generic.py
index df35a782d75..494af927f4e 100644
--- a/torchrl/collectors/distributed/generic.py
+++ b/torchrl/collectors/distributed/generic.py
@@ -37,6 +37,7 @@
 from torchrl.data.utils import CloudpickleWrapper
 from torchrl.envs.common import EnvBase
 from torchrl.envs.env_creator import EnvCreator
+from torchrl.weight_update.weight_sync_schemes import WeightSyncScheme
 
 SUBMITIT_ERR = None
 try:
@@ -461,6 +462,7 @@ def __init__(
         weight_updater: WeightUpdaterBase
         | Callable[[], WeightUpdaterBase]
         | None = None,
+        weight_sync_schemes: dict[str, WeightSyncScheme] | None = None,
     ):
 
         if collector_class == "async":
@@ -566,14 +568,61 @@ def __init__(
 
         self._init_workers()
         self._make_container()
-        if weight_updater is None:
-            weight_updater = DistributedWeightUpdater(
-                store=self._store,
-                policy_weights=self.policy_weights,
-                num_workers=self.num_workers,
-                sync=self._sync,
+
+        # Set up weight synchronization - prefer new schemes over legacy updater
+        if weight_updater is None and weight_sync_schemes is None:
+            # Default to Distributed weight sync scheme for distributed collectors
+            from torchrl.weight_update.weight_sync_schemes import (
+                DistributedWeightSyncScheme,
             )
-        self.weight_updater = weight_updater
+
+            weight_sync_schemes = {
+                "policy": DistributedWeightSyncScheme(backend=backend, sync=self._sync)
+            }
+
+        if weight_sync_schemes is not None:
+            # Use new weight synchronization system
+            self._weight_sync_schemes = weight_sync_schemes
+            self._weight_senders = {}
+
+            # Set up weight senders now that remote collectors exist
+            for model_id, scheme in self._weight_sync_schemes.items():
+                sender = scheme.create_sender()
+                sender._model_id = model_id
+
+                # Create transports for each remote collector
+                for i in range(self.num_workers):
+                    rank = i + 1  # Workers are 1-indexed in distributed
+                    transport = scheme.create_transport((self._store, rank))
+                    sender._transports[i] = transport
+
+                # Set context and register model
+                if hasattr(sender, "set_context"):
+                    sender.set_context(self, model_id)
+
+                # Store reference to source model for automatic extraction
+                if (
+                    model_id == "policy"
+                    and hasattr(self, "policy")
+                    and self.policy is not None
+                ):
+                    sender._source_model = self.policy
+
+                self._weight_senders[model_id] = sender
+
+            self.weight_updater = None
+        else:
+            # Fall back to legacy weight updater system
+            if weight_updater is None:
+                weight_updater = DistributedWeightUpdater(
+                    store=self._store,
+                    policy_weights=self.policy_weights,
+                    num_workers=self.num_workers,
+                    sync=self._sync,
+                )
+            self.weight_updater = weight_updater
+            self._weight_sync_schemes = None
+            self._weight_senders = {}
 
     @property
     def device(self) -> list[torch.device]:
@@ -928,6 +977,34 @@ def _next_async(self, total_frames, trackers):
                     break
         return data, total_frames
 
+    def _extract_weights_if_needed(self, weights: Any, model_id: str) -> Any:
+        """Extract weights from a model if needed.
+
+        For distributed collectors, when weights is None and we have a weight sync scheme,
+        extract fresh weights from the tracked policy model.
+        """
+        scheme = (
+            self._weight_sync_schemes.get(model_id)
+            if self._weight_sync_schemes
+            else None
+        )
+
+        if weights is None and scheme is not None:
+            # Extract fresh weights from the source model
+            sender = self._weight_senders.get(model_id)
+            if (
+                sender
+                and hasattr(sender, "_source_model")
+                and sender._source_model is not None
+            ):
+                # For distributed collectors, we need TensorDict format for isend/irecv
+                from tensordict import TensorDict
+
+                return TensorDict.from_module(sender._source_model).data.lock_()
+
+        # Fall back to base class implementation
+        return super()._extract_weights_if_needed(weights, model_id)
+
     def set_seed(self, seed: int, static_seed: bool = False) -> int:
         for i in range(self.num_workers):
             rank = i + 1
diff --git a/torchrl/collectors/distributed/ray.py b/torchrl/collectors/distributed/ray.py
index 97955c7c8b5..f2e00828dd3 100644
--- a/torchrl/collectors/distributed/ray.py
+++ b/torchrl/collectors/distributed/ray.py
@@ -28,6 +28,7 @@
 from torchrl.data import ReplayBuffer
 from torchrl.envs.common import EnvBase
 from torchrl.envs.env_creator import EnvCreator
+from torchrl.weight_update.weight_sync_schemes import WeightSyncScheme
 
 RAY_ERR = None
 try:
@@ -258,11 +259,15 @@ class RayCollector(DataCollectorBase):
 
             .. note:: although it is not enfoced (to allow users to implement their own replay buffer class), a
                 :class:`~torchrl.data.RayReplayBuffer` instance should be used here.
-        weight_updater (WeightUpdaterBase or constructor, optional): An instance of :class:`~torchrl.collectors.WeightUpdaterBase`
+        weight_updater (WeightUpdaterBase or constructor, optional): (Deprecated) An instance of :class:`~torchrl.collectors.WeightUpdaterBase`
             or its subclass, responsible for updating the policy weights on remote inference workers managed by Ray.
             If not provided, a :class:`~torchrl.collectors.RayWeightUpdater` will be used by default, leveraging
             Ray's distributed capabilities.
             Consider using a constructor if the updater needs to be serialized.
+        weight_sync_schemes (dict[str, WeightSyncScheme], optional): Dictionary mapping model identifiers to
+            :class:`~torchrl.weight_update.weight_sync_schemes.WeightSyncScheme` instances.
+            This is the recommended way to configure weight synchronization. If not provided,
+            defaults to ``{"policy": RayWeightSyncScheme()}``.
 
     Examples:
         >>> from torch import nn
@@ -326,6 +331,7 @@ def __init__(
         weight_updater: WeightUpdaterBase
         | Callable[[], WeightUpdaterBase]
         | None = None,
+        weight_sync_schemes: dict[str, WeightSyncScheme] | None = None,
     ):
         self.frames_per_batch = frames_per_batch
         if remote_configs is None:
@@ -436,9 +442,9 @@ def check_list_length_consistency(*lists):
         if not isinstance(policy_factory, Sequence):
             policy_factory = [policy_factory] * len(create_env_fn)
         self.policy_factory = policy_factory
-        self._local_policy = policy
-        if isinstance(self._local_policy, nn.Module):
-            policy_weights = TensorDict.from_module(self._local_policy)
+        self.policy = policy  # Store policy for weight extraction
+        if isinstance(policy, nn.Module):
+            policy_weights = TensorDict.from_module(policy)
             policy_weights = policy_weights.data.lock_()
         else:
             policy_weights = TensorDict(lock=True)
@@ -476,7 +482,10 @@ def check_list_length_consistency(*lists):
 
         # update collector kwargs
         for i, collector_kwarg in enumerate(self.collector_kwargs):
-            collector_kwarg["policy_factory"] = policy_factory[i]
+            # Don't pass policy_factory if we have a policy - remote collectors need the policy object
+            # to be able to apply weight updates
+            if policy is None:
+                collector_kwarg["policy_factory"] = policy_factory[i]
             collector_kwarg["max_frames_per_traj"] = max_frames_per_traj
             collector_kwarg["init_random_frames"] = (
                 init_random_frames // self.num_collectors
@@ -510,19 +519,84 @@ def check_list_length_consistency(*lists):
                 collector_kwargs,
                 remote_configs,
             )
-        if weight_updater is None:
-            weight_updater = RayWeightUpdater(
-                policy_weights=policy_weights,
-                remote_collectors=self.remote_collectors,
-                max_interval=self.max_weight_update_interval,
-            )
-        self.weight_updater = weight_updater
+        # Set up weight synchronization - prefer new schemes over legacy updater
+        if weight_updater is None and weight_sync_schemes is None:
+            # Default to Ray weight sync scheme for Ray collectors
+            from torchrl.weight_update.weight_sync_schemes import RayWeightSyncScheme
 
-        # Print info of all remote workers
-        pending_samples = [
-            e.print_remote_collector_info.remote() for e in self.remote_collectors
-        ]
-        ray.wait(pending_samples)
+            weight_sync_schemes = {"policy": RayWeightSyncScheme()}
+
+        if weight_sync_schemes is not None:
+            # Use new weight synchronization system
+            self._weight_sync_schemes = weight_sync_schemes
+            self._weight_senders = {}
+
+            # Set up weight senders now that remote collectors exist
+            for model_id, scheme in self._weight_sync_schemes.items():
+                sender = scheme.create_sender()
+                sender._model_id = model_id
+
+                # Register each remote collector as a separate worker
+                # This follows the same pattern as multiprocess collectors
+                for worker_idx, remote_collector in enumerate(self.remote_collectors):
+                    # Create a transport for this specific collector
+                    # Pass the collector as context so the transport knows which one to talk to
+                    sender.register_worker(worker_idx, remote_collector)
+
+                # Set context and register model
+                if hasattr(sender, "set_context"):
+                    sender.set_context(self, model_id)
+
+                # Store reference to source model for automatic extraction
+                if model_id == "policy":
+                    sender._source_model = self.policy
+
+                self._weight_senders[model_id] = sender
+
+            self.weight_updater = None  # Don't use legacy system
+        else:
+            # Fall back to legacy weight updater system
+            if weight_updater is None:
+                weight_updater = RayWeightUpdater(
+                    policy_weights=policy_weights,
+                    remote_collectors=self.remote_collectors,
+                    max_interval=self.max_weight_update_interval,
+                )
+            self.weight_updater = weight_updater
+            self._weight_sync_schemes = None
+            self._weight_senders = {}
+
+        # Print info of all remote workers (fire and forget - no need to wait)
+        for e in self.remote_collectors:
+            e.print_remote_collector_info.remote()
+
+    def _extract_weights_if_needed(self, weights: Any, model_id: str) -> Any:
+        """Extract weights from a model if needed.
+
+        For Ray collectors, when weights is None and we have a weight sync scheme,
+        extract fresh weights from the tracked policy model.
+        """
+        scheme = (
+            self._weight_sync_schemes.get(model_id)
+            if self._weight_sync_schemes
+            else None
+        )
+
+        if weights is None and scheme is not None:
+            # Extract fresh weights from the source model
+            sender = self._weight_senders.get(model_id)
+            if (
+                sender
+                and hasattr(sender, "_source_model")
+                and sender._source_model is not None
+            ):
+                from torchrl.weight_update.weight_sync_schemes import WeightStrategy
+
+                strategy = WeightStrategy(extract_as=scheme.strategy)
+                return strategy.extract_weights(sender._source_model)
+
+        # Fall back to base class behavior
+        return super()._extract_weights_if_needed(weights, model_id)
 
     @property
     def num_workers(self):
diff --git a/torchrl/collectors/distributed/rpc.py b/torchrl/collectors/distributed/rpc.py
index 5336b588232..9d2cf36c0cf 100644
--- a/torchrl/collectors/distributed/rpc.py
+++ b/torchrl/collectors/distributed/rpc.py
@@ -42,6 +42,7 @@
 from torchrl.data.utils import CloudpickleWrapper
 from torchrl.envs.common import EnvBase
 from torchrl.envs.env_creator import EnvCreator
+from torchrl.weight_update.weight_sync_schemes import WeightSyncScheme
 
 SUBMITIT_ERR = None
 try:
@@ -308,6 +309,7 @@ def __init__(
         weight_updater: WeightUpdaterBase
         | Callable[[], WeightUpdaterBase]
         | None = None,
+        weight_sync_schemes: dict[str, WeightSyncScheme] | None = None,
     ):
         if collector_class == "async":
             collector_class = MultiaSyncDataCollector
@@ -413,15 +415,63 @@ def __init__(
                 tensorpipe_options
             )
         self._init()
-        if weight_updater is None:
-            weight_updater = RPCWeightUpdater(
-                collector_infos=self.collector_infos,
-                collector_class=self.collector_class,
-                collector_rrefs=self.collector_rrefs,
-                policy_weights=self.policy_weights,
-                num_workers=self.num_workers,
-            )
-        self.weight_updater = weight_updater
+
+        # Set up weight synchronization - prefer new schemes over legacy updater
+        if weight_updater is None and weight_sync_schemes is None:
+            # Default to RPC weight sync scheme for RPC collectors
+            from torchrl.weight_update.weight_sync_schemes import RPCWeightSyncScheme
+
+            weight_sync_schemes = {"policy": RPCWeightSyncScheme()}
+
+        if weight_sync_schemes is not None:
+            # Use new weight synchronization system
+            self._weight_sync_schemes = weight_sync_schemes
+            self._weight_senders = {}
+
+            # Set up weight senders now that remote collectors exist
+            for model_id, scheme in self._weight_sync_schemes.items():
+                sender = scheme.create_sender()
+                sender._model_id = model_id
+
+                # Create transports for each remote collector
+                for i in range(self.num_workers):
+                    transport = scheme.create_transport(
+                        (
+                            self.collector_infos[i],
+                            self.collector_rrefs[i],
+                            self.collector_class,
+                        )
+                    )
+                    sender._transports[i] = transport
+
+                # Set context and register model
+                if hasattr(sender, "set_context"):
+                    sender.set_context(self, model_id)
+
+                # Store reference to source model for automatic extraction
+                if (
+                    model_id == "policy"
+                    and hasattr(self, "policy")
+                    and self.policy is not None
+                ):
+                    sender._source_model = self.policy
+
+                self._weight_senders[model_id] = sender
+
+            self.weight_updater = None
+        else:
+            # Fall back to legacy weight updater system
+            if weight_updater is None:
+                weight_updater = RPCWeightUpdater(
+                    collector_infos=self.collector_infos,
+                    collector_class=self.collector_class,
+                    collector_rrefs=self.collector_rrefs,
+                    policy_weights=self.policy_weights,
+                    num_workers=self.num_workers,
+                )
+            self.weight_updater = weight_updater
+            self._weight_sync_schemes = None
+            self._weight_senders = {}
 
     @property
     def device(self) -> list[torch.device]:
@@ -764,6 +814,34 @@ def _next_sync_rpc(self):
         self._collected_frames += data.numel()
         return data
 
+    def _extract_weights_if_needed(self, weights: Any, model_id: str) -> Any:
+        """Extract weights from a model if needed.
+
+        For RPC collectors, when weights is None and we have a weight sync scheme,
+        extract fresh weights from the tracked policy model.
+        """
+        scheme = (
+            self._weight_sync_schemes.get(model_id)
+            if self._weight_sync_schemes
+            else None
+        )
+
+        if weights is None and scheme is not None:
+            # Extract fresh weights from the source model
+            sender = self._weight_senders.get(model_id)
+            if (
+                sender
+                and hasattr(sender, "_source_model")
+                and sender._source_model is not None
+            ):
+                from torchrl.weight_update.weight_sync_schemes import WeightStrategy
+
+                strategy = WeightStrategy(extract_as=scheme.strategy)
+                return strategy.extract_weights(sender._source_model)
+
+        # Fall back to base class implementation
+        return super()._extract_weights_if_needed(weights, model_id)
+
     def set_seed(self, seed: int, static_seed: bool = False) -> int:
         for worker in self.collector_infos:
             seed = rpc.rpc_sync(worker, self.collector_class.set_seed, args=(seed,))