Large Language Models (LLMs) are increasingly used in multi-turn agentic workloads that require sustained interaction with complex environments. Existing reflective prompt optimizers such as GEPA lear
GEPA-Curator improves agentic prompt optimization by filtering unreliable failure signals and retrieving past successes, boosting performance across multiple benchmarks.
Large Language Models (LLMs) are increasingly used in multi-turn agentic workloads that require sustained interaction with complex environments. Existing reflective prompt optimizers such as GEPA learn from failed rollouts, but agentic failures can also come from prompt weaknesses, harness limitations, stochastic execution, or noisy judges. We argue that such failures may not provide useful learning signal, and may even be harmful; and thus it is essential to curate which tasks are provided to the prompt optimizer for better performance. We introduce GEPA-Curator, a framework that augments the prompt optimizer to select which agent experiences should shape prompt optimization. In…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
GEPA-Curator: Curated Learning for Self Improving Agents was first seen on Hugging Face · Daily papers on Nov 3, 2026. Since then it has appeared in 6 checks on 1 different day. Its best position was #26 of 30, reached on Nov 3, 15:28 UTC. It dropped off the list after Nov 3, 17:28 UTC.
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Not at the moment. It was last on the list on Nov 3, 17:28 UTC, after 1 day of trending.
#26 on Hugging Face · Daily papers, on Nov 3, 15:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 3, 2026 and has been on the list on 1 day (6 checks) since.