gepa-reflective-prompt-evolution-2025

Summary

Instead of leaning on expensive reward-signal training, GEPA optimizes prompts through a lightweight cycle of draft, score, critique, and revise, and in some benchmarks that simpler loop actually beats reinforcement learning. The practical takeaway is that structured self-reflection can be a more cost-effective route to better prompts than heavy training infrastructure, which matters for any system trying to improve its own instructions without a full RL pipeline.