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Maximizing Action Influence to Train Controllable World Models

A CoRL 2026 paper proposes the Action Influence Maximization (AIM) algorithm, which actively collects training data to increase the action influence of the dataset, thereby improving the controllability of action-conditioned video world models. The authors prove that a data's action influence metric upper-bounds the action influence any model trained on that data can express, and that the narrow action distribution of expert demonstrations causes this ceiling to be low.

Part of the event: CoRL 2026 Paper Proposes AIM Algorithm to Improve World Model Controllability

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