diff --git a/docs/docs/learn/optimization/optimizers.md b/docs/docs/learn/optimization/optimizers.md
index a684f6f5b4..1ed0b08bba 100644
--- a/docs/docs/learn/optimization/optimizers.md
+++ b/docs/docs/learn/optimization/optimizers.md
@@ -58,7 +58,7 @@ These optimizers produce optimal instructions for the prompt and, in the case of
 
 6. [**`MIPROv2`**](../../api/optimizers/MIPROv2.md): Generates instructions *and* few-shot examples in each step. The instruction generation is data-aware and demonstration-aware. Uses Bayesian Optimization to effectively search over the space of generation instructions/demonstrations across your modules.
 
-7. [**`SIMBA`**](../../api/optimizers/SIMBA.md)
+7. [**`SIMBA`**](../../api/optimizers/SIMBA.md): Samples mini-batches, identifies challenging examples with high output variability, and improves the program by either adding successful examples as demonstrations or using the LLM to compare good vs. bad trajectories and generate improvement instructions.
 
 8. [**`GEPA`**](../../api/optimizers/GEPA/overview.md): Uses LM's to reflect on the DSPy program's trajectory, to identify what worked, what didn't and propose prompts addressing the gaps. Additionally, GEPA can leverage domain-specific textual feedback to rapidly improve the DSPy program. Detailed tutorials on using GEPA are available at [dspy.GEPA Tutorials](../../tutorials/gepa_ai_program/index.md).