Rollout Degeneration under Unsupported Actions in World Models

Autoregressive world models often degenerate over long rollouts, a problem typically attributed to the train-test mismatch between clean training contexts and imperfect model-generated visual contexts. We show that world models face an additional source of instability: externally supplied actions can induce state-action pairs outside the regime covered during training. Through controlled action-space manipulations, we find that such *unsupported actions* substantially destabilize ongoing rollouts. Beyond the conventional degeneration pattern of progressive off-support drift, this instability can manifest as *conservative collapse*, where severely degraded visual states abruptly transition to visually plausible but dynamically unreliable states. Based on this analysis, we propose *validity-aware action gating* to mitigate degeneration under unsupported actions by estimating whether the state-action pair lies within the training support and adapting the influence of action conditioning accordingly. Experiments in visual navigation and robotic manipulation show that our method consistently reduces late-horizon degeneration under unsupported actions, lowering final 16-second FID by 45.3% and 13.3% over the original models in the two domains, respectively, while preserving competitive performance under supported actions.