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agent learning

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papersTODAY 04:00 UTC

Co-Training Policy and World Models Improves LLM Agent Learning

A new arXiv paper proposes jointly training a language model agent's policy alongside a world model, so the agent learns both which actions earn rewards and how those actions change the environment. The authors argue that standard reinforcement learning gives sparse guidance about environmental consequences, and that combining policy learning with world modeling addresses this gap. The work targets improved decision-making for LLM-based agents in interactive settings.

agents/world-models/reinforcement-learning/research