新的Muon方法增强了长时序智能体训练

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-14 00:00

研究人员为长时序智能体开发了一种新的训练方法。这类智能体旨在协调推理、工具使用和环境反馈，以应对长时间任务。该方法名为Muon，采用了单轨迹异步学习、token平均损失、截断重要性采样以及长度和工具错误辅助惩罚。与基线方法相比，该方法在存储库级别的问题解决任务中，能够实现异步更新，无需等待多个轨迹，从而提高了训练成功率。 AI

影响这种新的训练方法有望提升AI智能体在复杂、长期任务中的能力。

排序理由该集群描述了一篇研究论文，其中详细介绍了一种新的AI智能体训练方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

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Newsworthiness bucket: Tool; 该集群描述了一篇研究论文，其中详细介绍了一种新的AI智能体训练方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

Hugging Face Daily Papers TIER_1 Nederlands(NL) · 2026-10-14 00:00

Long-Horizon RL Needs Muon

Long-horizon agents must coordinate reasoning, tool use, and environment feedback over extended interactions. Learning from sparse outcome rewards requires weighting trajectories of different lengths and updating the policy while rollouts are still being collected. We examine a s…

本文如何被排名

报道来源 [1]

Long-Horizon RL Needs Muon

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