Beating RL With Reflection: GEPA and Optimize Anything — Lakshya A. Agrawal, GEPA
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AI Engineer
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AI Engineer
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Key takeaways · AI-distilled
Agrawal reports that one round of reflection on three examples let GEPA double the gains GRPO reached after 25,000 rollouts. RL squeezes a rollout into one score; GEPA has a model read the full trace, including reasoning, tool calls and errors, and write a better prompt.
GEPA keeps a Pareto pool of candidates rather than one running best, which Agrawal says keeps the search out of local optima.
Optimize Anything applies the idea to anything expressible as text with a score. He shows it growing a four-line program into a six-step AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → that lifts Gemini Flash on ARC-AGI from 32.5% to 89.5%, and an AMD NPU coding agent from 4% to 30%.
Learned skills raised a GPT-5 mini coding agent on Go issues from 24% to 93% and carried over to Claude Sonnet. He also cites Databricks tuning an open model to beat Claude Opus at 90x lower cost.
Terms in this piece · Glossary
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
