Beating RL With Reflection: GEPA and Optimize Anything — Lakshya A. Agrawal, GEPA

This post introduces GEPA (Generative Exploration and Prompting Agent) and Optimize Anything, frameworks that leverage reflective optimization for AI tasks. GEPA significantly improves sample efficiency in reinforcement learning by having an AI model read full traces (thoughts, tool calls, errors) and write better prompts, outperforming traditional RL algorithms like GRPO with far fewer examples. Optimize Anything extends this to any text-based, scoreable task, demonstrating dramatic improvements in agent performance across various benchmarks (e.g., ARC-AGI, AMD NPU coding, GPT-5 mini coding). The work also touches on learning evals from production traces and co-optimizing prompts and weights.