English(EN) Policy and World Modeling Co-Training for Language Agents

新框架联合训练语言代理的策略和世界模型

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-01 15:35

研究人员开发了一个名为PaW的新框架来训练语言代理。该方法在强化学习过程中同时联合训练策略和世界模型组件。PaW利用现有的RL数据来提供世界模型监督，避免了对单独模拟器或额外计算的需求。 AI

影响通过将世界模型与强化学习相结合，引入了一种更有效的训练语言代理的方法。

排序理由该集群包含一篇详细介绍训练AI代理新研究框架的学术论文。

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

报道来源 [2]

arXiv cs.AI TIER_1 English(EN) · Ning Lu, Baijiong Lin, Shengcai Liu, Jiahao Wu, Haoze Lv, Yanbin Wei, Lingting Zhu, Shengju Qian, Xin Wang, Ying-Cong Chen, Qi Wang, Ke Tang · 2026-06-02 04:00

Policy and World Modeling Co-Training for Language Agents

arXiv:2606.02388v1 Announce Type: cross Abstract: Reinforcement learning (RL) improves large language model (LLM) agents by teaching them which actions lead to high rewards, but provides little supervision on what those actions do to the environment. World modeling (WM) can fill …
arXiv cs.AI TIER_1 English(EN) · Ke Tang · 2026-06-01 15:35

Policy and World Modeling Co-Training for Language Agents

Reinforcement learning (RL) improves large language model (LLM) agents by teaching them which actions lead to high rewards, but provides little supervision on what those actions do to the environment. World modeling (WM) can fill this gap, yet existing approaches often require se…