🔥 GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning

💡 The paper introduces GEPA, a prompt optimizer that uses natural language reflection to learn high level rules from trial and error, outperforming reinforcement learning methods. The problem addressed is that current reinforcement learning methods, such as Group Relative Policy Optimization, require thousands of rollouts to learn new tasks, which can be time consuming and inefficient. The authors argue that the interpretable nature of language can provide a richer learning medium for large language models compared to policy gradients derived from sparse scalar rewards.

The method used is GEPA, a Genetic-Pareto prompt optimizer that incorporates natural language reflection to learn high level rules from trial and error. GEPA samples system level trajectories, reflects on them in natural language to diagnose problems, proposes and tests prompt updates, and combines complementary lessons from its own attempts. This approach allows GEPA to turn even a few rollouts into a large quality gain.

The results show that GEPA outperforms Group Relative Policy Optimization by 10 percent on average and by up to 20 percent, while using up to 35 times fewer rollouts. GEPA also outperforms the leading prompt optimizer, MIPROv2, by over 10 percent across two large language models. Additionally, GEPA demonstrates promising results as an inference time search strategy for code optimization. Overall, the paper contributes a new approach to prompt optimization that can efficiently learn high level rules from trial and error, outperforming current reinforcement learning methods.

📅 Published on Jul 25, 2025

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2507.19457
• PDF: https://arxiv.org/pdf/2507.19457
• Project Page: https://gepa-ai.github.io/gepa/

🤖 Models citing this paper:
• https://huggingface.co/pirola/local-ai-coding-stack-research

📊 Datasets citing this paper:
• https://huggingface.co/datasets/zhongweixie/A-Survey-on-AI-Agent-Harness

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📢 By: https://xn--r1a.website/PaperNexus

#NaturalLanguageReflection #PromptOptimization #ReinforcementLearningAlternatives #GeneticParetoOptimization #LanguageModelLearning