Zack Yu, Nicholas Lee, Minjae Lee, Lakshya Agarwal, Luca Manolache, Wonjun Kang, Matei Zaharia, Michael W. Mahoney 等 11 位作者
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 particular, GEPA-Curator consists of two components: task curation, which filters out tasks that are likely to provide noisy or unreliable learning signals, and prior success retrieval, which selectively retrieves past successful trajectories for a task and provides them as additional context to the prompt optimizer. We evaluate GEPA-Curator on GoBrowse, ALFWorld, AppWorld, and $τ$-bench, using both Gemini 3.1 Pro and Claude Opus 5 as optimizer models. GEPA-Curator matches or exceeds the mean performance of both baselines, with gains of up to 20.7% over GEPA and 21.2% over ACE.
Immanuel Abdi Facebook, Akshat Gupta University of California, Berkeley, Micah Mok, Alex Lu, Nicholas Lee University of California, Berkeley, Gopala Anumanchipalli University of California, Berkeley
One of biggest missing capabilities in state-of-the-art AI systems is the ability to learn continually after deployment. However, implementing an inference-time learning system has several challenges including the large memory requirement of gradient-based algorithms that are used to train state-of-the-art LLMs. Evolutionary Strategies (ES) have recently re-emerged as a gradient-free alternative to traditional learning algorithms and have shown encouraging performance on specific tasks in LLMs. In this paper, we perform a more comprehensive analysis of ES and specifically evaluate its forgetting curves when training for a larger number of update steps. We find that although ES is able to reach performance numbers closer to GRPO for math and reasoning tasks, it is accompanied by significant forgetting of prior abilities. We also show that the updates made using ES are much less sparse and have a larger l2 norm compared to corresponding GRPO updates, explaining the contrasting forgetting curves between the two algorithms. With this study, we aim to specifically highlight the issue of forgetting in gradient-free algorithms like ES and hope to inspire future work to mitigate these issues.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · 2026 · 正式发表