GEPA-Curator: Curated Learning for Self Improving Agents
GEPA-Curator filters noisy agent failures and retrieves relevant successful runs before prompt optimization, improving results over two baselines across four environments.
GEPA-Curator adds **task curation** to filter experiences with unreliable learning signals and **prior success retrieval** to add relevant successful trajectories as context. It was evaluated on four environments with Gemini 3.1 Pro and Claude Opus 5 as optimizer models.
If you optimize prompts from coding-agent runs, separate useful failures from harness issues, stochastic outcomes, and noisy judges; consider reusing successful trajectories for similar tasks.
GEPA-Curator adds **task curation** to filter experiences with unreliable learning signals and **prior success retrieval** to add relevant successful trajectories as context. It was evaluated on four environments with Gemini 3.1 Pro and Claude Opus 5 as optimizer models. If you optimize prompts from coding-agent runs, separate useful failures from harness issues, stochastic outcomes, and noisy judges; consider reusing successful trajectories for similar tasks. The paper reports gains of up to **20.7% over GEPA** and **21.2% over ACE**, but the material does not specify how these gains vary by environment or how much curation costs.
GEPA-Curator adds two controls to prompt optimization: filter experiences with unreliable learning signals and retrieve relevant successful trajectories. The reported gains are bounded by four environments and two optimizer models; the supplied record leaves variation by environment and curation cost unspecified.