Lakshya Agrawal presented GEPA, a reflective optimization framework that improves AI agents by analyzing text traces and iteratively editing prompts rather than tuning weights. The method beats reinforcement learning using a fraction of the data while optimizing entire code harnesses and scheduling policies.
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