English(EN) SAPD: Learning from Agent Experience without Environment Interaction

新的SAPD方法在无需与环境交互的情况下，利用离线数据训练LLM代理

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-21 04:00

研究人员开发了一种名为Step-Advantage Policy Distillation (SAPD) 的新方法，以使用现有的离线数据更有效地训练大型语言模型 (LLM) 代理。这种方法允许代理从过去的交互中学习，而无需重新访问环境，从而避免了监督微调中常见的灾难性遗忘。SAPD分解了多轮轨迹，并使用相对步优势信号来稳定训练，在ALFWorld和WebShop等基准测试中，与现有方法相比，其性能有所提高，训练速度也更快。 AI

影响 SAPD通过更有效地利用现有数据，可以显著降低训练LLM代理所需的计算成本和时间。

排序理由该集群包含一篇详细介绍新AI模型训练方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Juncheng Diao, Haibin Wang, Wei Shi, Yifan Ding, Qingbin Li, Rongxiang Weng, Xunliang Cai · 2026-10-21 04:00

SAPD：无需环境交互即可从智能体经验中学习

arXiv:2610.27847v1 Announce Type: new Abstract: Scaling the training of Large Language Model (LLM) agents typically requires a large collection of executable environments, whose construction, maintenance, and operation constrain the expansion of training experience. Existing offl…

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报道来源 [1]

SAPD：无需环境交互即可从智能体经验中学习

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