OneWM unified causal training framework validates mutual gains between world models and action learning

The paper proposes a unified causal training framework, OneWM, in which actions serve simultaneously as conditions for world prediction and targets for policy learning within a shared video-action architecture. The authors evaluate policy learning and world modeling on LIBERO, LIBERO-Plus, and RoboCasa365, supplemented by real-world policy experiments: learning the visual consequences of specified actions improves policy performance and cross-scene generalization, while action learning under the same architecture improves the fidelity of multi-view world prediction. Positive results were obtained for mutual gains in both directions.