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English(EN) Beyond Outcome Feedback: Trajectory-Quality-Guided Experience-based Learning

新的LLM学习框架使用轨迹质量来提高代理性能

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种新颖的LLM代理基于经验的学习框架TQG-Exp。该方法通过引入轨迹质量损失（TQL）信号，超越了传统的结果反馈，加入了过程反馈。TQL评估任务完成度和执行过程的质量，通过配对的rollouts来指导经验优化。在ALFWorld、WebShop和ScienceWorld上的实验表明，TQG-Exp显著提高了性能，在DeepSeek-V4-Flash和Qwen3.5-Flash模型上优于现有方法。 AI

影响 这个新的学习框架可以通过优化整个执行过程而不仅仅是最终结果，来提高LLM代理在复杂任务中的效率和有效性。

排序理由 该集群包含一篇详细介绍LLM代理新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的LLM学习框架使用轨迹质量来提高代理性能

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该集群包含一篇详细介绍LLM代理新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Bingjie Zhang, Yibo Yang, Qiantong Liu, Benshuo Fu, Yi Chang, Dandan Guo ·

    超越结果反馈：轨迹质量指导的基于体验的学习

    arXiv:2610.14877v1 Announce Type: new Abstract: Experience-based learning enables large language model (LLM) agents to improve over time by reusing knowledge from past executions. However, existing methods typically rely on final task outcomes as feedback for experience learning,…

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