diff --git a/docs/docs/learn/optimization/optimizers.md b/docs/docs/learn/optimization/optimizers.md
index a684f6f5b4..253807cf32 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): Uses stochastic mini-batch sampling to identify challenging examples with high output variability, then applies the LLM to introspectively analyze failures and generate self-reflective improvement rules or add successful demonstrations.
 
 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).