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

Researchers have developed a new framework called PaW that co-trains policy and world modeling for language agents. This approach integrates world modeling supervision directly into reinforcement learning, using existing on-policy RL rollouts to teach agents about the consequences of their actions. PaW introduces novel components for data selection, noise tolerance, and adaptive loss balancing, demonstrating consistent improvements on agentic task benchmarks. AI

IMPACT Enhances language agent capabilities by improving their understanding of environmental dynamics and action consequences.

RANK_REASON The cluster contains a research paper detailing a new framework for training language agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Language Agents
• Reinforcement Learning
• World Modeling

• paper
• model release

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