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**🚀 Featured Example:** Train LLMs to play BlackJack using [torchforge](https://github.com/meta-pytorch/torchforge) (PyTorch's agentic RL framework) + OpenEnv
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**🚀 Featured:** Train LLMs to play BlackJack using [torchforge](https://github.com/meta-pytorch/torchforge) (PyTorch's agentic RL framework):
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→ See full tutorial: [`examples/grpo_blackjack/`](examples/grpo_blackjack/)
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## OpenEnv on partner platforms:
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- [Lightning AI Studio](https://lightning.ai/environments?section=featured)
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- TRL Example (Coming Soon)!
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- Unsloth Google Colab (Coming soon)!
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## Overview
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OpenEnv provides a standard for interacting with agentic execution environments via simple Gymnasium style APIs - step(), reset(), state(). Users of agentic execution environments can interact with the environment during RL training loops using these simple APIs.

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