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数据提供世界模型监督，避免了对独立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。
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新框架联合训练语言代理的策略和世界模型
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报道来源 [3]
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语言代理的策略与世界模型联合训练
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 …
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语言代理的策略与世界模型联合训练
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…
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语言代理的策略与世界模型联合训练
PaW is a co-training framework that combines policy learning and world modeling using on-policy reinforcement learning rollouts to improve language agent training without additional computational overhead.