AI RESEARCH
Enhancing Policy Learning with World-Action Model
arXiv CS.AI
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ArXi:2603.28955v1 Announce Type: new This paper presents the World-Action Model (WAM), an action-regularized world model that jointly reasons over future visual observations and the actions that drive state transitions. Unlike conventional world models trained solely via image prediction, WAM incorporates an inverse dynamics objective into DreamerV2 that predicts actions from latent state transitions, encouraging the learned representations to capture action-relevant structure critical for downstream control.