How does prompt optimization compare to RL algos like GRPO?
GRPO needs 1000s of rollouts, but humans can learn from a few trials—by reflecting on what worked & what didn't.
Meet GEPA: a reflective prompt optimizer that can outperform GRPO by up to 20% with 35x fewer rollouts!🧵
We implemented GEPA as a new @DSPyOSS optimizer (release soon!). This means that it works for even sophisticated agents or compound systems you've already implemented.
GEPA outperforms the MIPROv2 optimizer by as much as 11% across 4 tasks for Qwen3 and GPT-4.1-mini.
Of course: Weight updates remain necessary to teach the models completely new tasks and still excel at general-purpose (massively multi-task!) post-training!
However, we show that for specialization to downstream systems, reflective prompt optimization can go really far with tiny data sizes and rollout budgets!
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GEPA builds a Pareto tree of proposed prompts.
In each step, GEPA picks a prompt P that has performed best on some examples—even if not best overall!
GEPA performs a few rollouts with P, and uses NL reflection on the resulting trajectories to extract a few lessons, which GEPA validates on a few examples.
If they work, these lessons become part of a new node in the Pareto tree. This allows GEPA to propose increasingly nuanced prompts as the optimization progresses.
This design gives GEPA two additional, bonus features:
(1) GEPA’s prompts are not only more effective but also up to 9x shorter than those from leading few-shot optimizers!
(2) GEPA shows promise as an inference-time search technique. It can generate performant kernels for AMD’s latest NPUs, outperforming RAG and iterative refinement techniques.
Paper: arxiv.org/abs/2507.19457
GEPA will be open-sourced soon as a new DSPy optimizer. Stay tuned!
Incredibly grateful to the wonderful team @ShangyinT @dilarafsoylu @NoahZiems @rishiskhare @kristahopsalong @arnav_thebigman @krypticmouse @michaelryan207 @Meng_CS @ChrisGPotts @koushik77 @AlexGDimakis @istoica05, Dan Klein, @matei_zaharia @lateinteraction
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