Integrating world models with RL for LLM agents

Establish methods to seamlessly integrate learned world models with reinforcement learning for language-model-based agents, enabling reliable state representation and reward generation in complex environments.

Background

Model-based RL depends on informative state representations and scalable, robust rewards. The survey highlights recent advances in generative world models and video pretraining, but notes that connecting such models with RL for LLM agents remains unresolved. Addressing this would enable richer, interactive training environments for language agents.