arxiv:2610.21095

Can World and Action Modeling Benefit Each Other under Unified Causal Training?

Published on Oct 15

· Submitted by

Selen Su on Oct 16

Interactive and Active AI

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Authors:

Yue Su ,

Abstract

OneWM unifies causal training of world and action modeling, showing that learning action-conditioned dynamics improves policies and action learning improves multi-view world prediction.

Generated by thinkingmachines/Inkling-Small

World and action modeling are closely related, yet their reciprocal benefits under unified causal training remain unclear. We introduce OneWM, a simple framework for unified causal training that treats actions as both conditions for world prediction and targets for policy learning within a shared video-action architecture. Using this framework, we investigate whether learning forward dynamics conditioned on specified actions improves action learning, and whether learning to generate actions in turn improves action-conditioned world modeling. We use LIBERO, LIBERO-Plus, and RoboCasa365 as embodied testbeds to evaluate both policy learning and world modeling, complemented by real-world policy experiments. Learning the visual consequences of specified actions improves policy performance and generalization across changing scenes. In turn, action learning improves multi-view world-prediction fidelity under the same architecture. Together, these results answer both questions positively, supporting reciprocal benefits between world modeling and action generation under unified causal training.

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OneWM — official resources

Real-world robot demonstrations

Full robot recordings at original speed, without audio.

Block stacking — stacking three colored blocks.

Storage — inserting a blue block into a storage unit.

Breakfast preparation — plating toast and a fried egg.

An Endless Dream

An imagined rollout starting from initial scene and wrist-camera observations. OneWM alternates action prediction and future-frame generation, using its own predictions to continue the rollout. Frames after the initial observations are model-generated; the predicted actions are not executed in a simulator.

Alphabet soup into the basket

Two objects into the basket

The first 16 seconds at original speed, with synchronized scene and wrist views plus predicted actions: the relative control path, translation, rotation, and gripper values. The path accumulates saved translation commands within each round; it is not a measured robot trajectory.