Training Controllable World Models via Action Influence Maximization
Action-conditioned video generative models trained on successful, goal-directed demonstrations tend to ignore the commanded action, rendering a plausible expert-like future regardless of what is asked of them. We trace this failure to training data that lack sufficient action influence. We analyze this metric and prove that it upper bounds the action influence that any model trained on that data can express. Expert demonstrations, whose narrow action distributions let next states be predicted without attending to the action, set a low ceiling that no amount of model capacity alone can raise. Raising it requires collecting different data. We introduce Action Influence Maximization (AIM), a...
| Authors | Samuel M. Bateman, Tenny Yin, Chongyi Zheng et al. |
|---|---|
| Primary category | cs.RO |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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