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English(EN) TSBPO: Tree-Structured Branching for Policy Optimization in Interactive Language Agents

新的TSBPO方法优化交互式语言代理

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种名为TSBPO的新型训练范式，旨在优化交互式语言代理。该方法解决了采样任务续接、分配延迟反馈以及聚合多token动作的token损失等挑战。TSBPO采用树状结构，并结合了似然拟合分配器，以目标那些可能产生不同终端结果的续接。当应用于Qwen2.5-1.5B actor时，TSBPO在ALFWorld和WebShop基准测试中取得了高成功率，优于标准的训练配置。 AI

影响 引入了一种新颖的训练方法，有望提高交互式语言代理在复杂任务上的性能和效率。

排序理由 该集群包含一篇学术论文，详细介绍了一种训练语言代理的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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新的TSBPO方法优化交互式语言代理

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该集群包含一篇学术论文，详细介绍了一种训练语言代理的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Rui Yu, Chong Mu, Jinchuan Zhang ·

    TSBPO: Tree-Structured Branching for Policy Optimization in Interactive Language Agents

    arXiv:2610.41697v1 Announce Type: cross Abstract: Training interactive language agents from terminal outcomes poses three coupled problems: which task and state continuations to sample, how to assign delayed feedback to decisions, and how to aggregate token losses for multi-token…

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