GEPA

GEPA (Genetic-Pareto) is a prompt optimization framework for LLMs: it uses natural language reflection to evolve instructions, achieving up to 35x greater sample efficiency than traditional Reinforcement Learning (RL) methods.

This is a paradigm shift in Large Language Model (LLM) tuning. GEPA (Genetic-Pareto) employs an evolutionary search loop: it uses a powerful reflection model to analyze execution traces, diagnose problems, and propose targeted prompt updates. This method leverages rich, textual feedback—not sparse scalar rewards—to learn high-level rules from trial and error. On complex reasoning tasks, GEPA consistently outperforms baselines like Group Relative Policy Optimization (GRPO), requiring up to 35 times fewer rollouts to achieve superior performance. The framework is available via a simple `pip install gepa` command, with direct integration into systems like DSPy.

https://github.com/gepa-ai/gepa