English(EN) Evolving to Match Capabilities: Improving Harness Self-Evolution via Skill Adaptation

新研究提出技能适应以提升LLM代理的自我演进能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

一篇新研究论文探讨了如何提升大型语言模型代理的自我演进能力。研究发现，对更强大的模型有益的技能有时会阻碍较弱的模型，导致性能下降。为解决此问题，研究人员开发了一种“技能适应”方法，重新构建现有技能以更好地适应执行器模型的性能。这种由元技能指导、并由Kimi-K3等模型执行的适应性改进，将较弱模型的性能提升了高达14.1个点。当集成到自我演进循环中时，这种元技能在各种模型上持续提高了最终得分。 AI

影响这项研究可能带来更有效的LLM代理，它们可以在不进行直接参数更新的情况下适应和改进其性能。

排序理由学术论文，详细介绍了一种改进LLM代理能力的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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Newsworthiness bucket: Tool; 学术论文，详细介绍了一种改进LLM代理能力的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Zhihao Yang, Haojin Wang, Juncheng Wu, Kuan Zhang, Yukun Chen, Run Luo, Yaojie Lu, Yuyin Zhou, Lu Wang, Xianpei Han · 2026-10-27 04:00

能力演进与匹配：通过技能适应改进Harness的自我演进

arXiv:2610.33024v1 Announce Type: cross Abstract: Harness self-evolution offers a practical route for LLM agents to improve from execution experience without updating model parameters. Its effectiveness depends on whether models can successfully act on the guidance provided by th…

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报道来源 [1]

能力演进与匹配：通过技能适应改进Harness的自我演进

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