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English(EN) TRACE: Turn-level Reward Assignment via Credit Estimation for Long-Horizon Agents

新的TRACE方法增强了AI智能体在长时程任务中的工具使用 · 跟踪2个来源

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了TRACE，一种用于提高多回合AI智能体在复杂、长时程任务中性能的新方法。该技术通过从参考模型的对数概率中推导出每个动作的奖励，而不是仅仅依赖稀疏的结果奖励，来解决信用分配的挑战。TRACE显著提升了Qwen3-4B和Qwen3-30B-A3B等模型在BrowseComp-Plus等基准测试中的工具使用能力，从而加快了收敛速度并改善了学习曲线。 AI

影响 增强了AI智能体在复杂、多回合任务中的能力，可能加速需要长时程推理的领域的进展。

排序理由 该集群包含一篇详细介绍AI智能体新方法的论文。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新的TRACE方法增强了AI智能体在长时程任务中的工具使用 · 跟踪2个来源

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Leitian Tao, Baolin Peng, Wenlin Yao, Tao Ge, Hao Cheng, Mike Hang Wang, Jianfeng Gao, Sharon Li ·

    TRACE：通过信用估计为长时域智能体分配回合级奖励

    arXiv:2607.13988v1 Announce Type: new Abstract: Multi-turn agents solve complex tasks through extended sequences of tool interactions before producing a final answer, making credit assignment a fundamental challenge during post-training. Outcome rewards provide reliable supervisi…

  2. arXiv cs.LG TIER_1 English(EN) · Sharon Li ·

    TRACE：通过信用估计实现长时域智能体的回合级奖励分配

    Multi-turn agents solve complex tasks through extended sequences of tool interactions before producing a final answer, making credit assignment a fundamental challenge during post-training. Outcome rewards provide reliable supervision for short-horizon reasoning, but become spars…

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