Joint Training and Evaluation of World Models in Non-Stationary Environments

Investigate joint training, continual updating, and rigorous evaluation protocols for world models used by large language model-based agents in non-stationary environments, and ascertain the causal impact of these world models on downstream planning reliability.

Background

World-model-based agents aim to mitigate myopic reasoning via internal simulation and lookahead. Although model-based RL systems like DreamerV3 show the effectiveness of imagined rollouts, current LLM-based agents often rely on ad hoc representations trained on short-horizon, environment-specific data.

Only a few efforts explore co-evolving world models and agents over time. Establishing methods to jointly train, update, and evaluate world models under non-stationarity—and to quantify their causal influence on planning—remains a core challenge.