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New framework co-trains policy and world modeling for language agents

By PulseAugur Editorial · [3 sources] ·

Researchers have developed a new framework called PaW for training language agents. This method co-trains policy and world modeling components simultaneously during reinforcement learning. PaW leverages existing RL data to provide world modeling supervision, avoiding the need for separate simulators or additional computation. AI

IMPACT Introduces a more efficient method for training language agents by integrating world modeling with reinforcement learning.

RANK_REASON The cluster contains an academic paper detailing a new research framework for training AI agents.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New framework co-trains policy and world modeling for language agents

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The cluster contains an academic paper detailing a new research framework for training AI agents.
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COVERAGE [3]

  1. 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 ·

    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 …

  2. arXiv cs.AI TIER_1 English(EN) · Ke Tang ·

    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…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    Policy and World Modeling Co-Training for Language Agents

    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.

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