Policy and World Modeling Co-Training for Language Agents
Summary
PaW, a Policy and World modeling co-training framework, enhances large language model (LLM) agents by integrating world modeling (WM) supervision directly into reinforcement learning (RL). Traditional RL improves agents by teaching high-reward actions but offers limited insight into environmental changes caused by those actions. Existing WM approaches often demand separate simulators, additional training stages, or extra inference-time computation. PaW addresses this by leveraging on-policy RL rollouts, which inherently contain action-to-next-observation signals, to provide auxiliary WM supervision to the policy during RL without altering the inference paradigm. The framework incorporates three key components: action-entropy-based WM data selection, a noise-tolerant WM loss, and reward-adaptive loss balancing. Experiments across three agentic task benchmarks demonstrate consistent improvements over strong RL baselines, indicating that standard RL rollouts are a practical source for WM supervision in language-agent training.
Key takeaway
For Machine Learning Engineers developing large language model agents, if you are struggling with agents that perform actions without understanding environmental consequences, you should consider integrating PaW's co-training framework. This approach enables you to utilize existing RL rollouts for world modeling, improving agent performance on complex tasks without needing separate simulators or additional inference-time computation. Implement its data selection and loss balancing components to ensure stable and informative auxiliary supervision.
Key insights
Integrating world modeling directly into RL using existing on-policy rollouts significantly improves LLM agent performance.
Principles
- On-policy RL rollouts contain sufficient WM signal.
- Auxiliary WM supervision can be added without inference changes.
- Robust WM requires data selection, noise tolerance, and loss balancing.
Method
PaW co-trains policy and world models by adding auxiliary WM supervision to the policy during RL. It uses action-entropy-based data selection, noise-tolerant WM loss, and reward-adaptive loss balancing.
In practice
- Apply PaW to enhance LLM agents in complex environments.
- Utilize existing RL rollouts for implicit world model training.
- Implement robust WM loss for noisy observation spaces.
Topics
- Reinforcement Learning
- World Modeling
- Language Agents
- LLM Agents
- PaW Framework
- Co-training
Best for: Research Scientist, AI Scientist, Machine Learning Engineer
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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.