From 5c4298a5dfd7700f7a4e52d125c6e55d1a1ff188 Mon Sep 17 00:00:00 2001 From: TomuHirata Date: Thu, 6 Nov 2025 14:34:23 +0900 Subject: [PATCH] Update SIMBA optimizer documentation to include detailed description of its functionality Signed-off-by: TomuHirata --- docs/docs/learn/optimization/optimizers.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) 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).