On-Policy Adversarial Fine-Tuning for Long-Horizon Autoregressive Video Generation

Autoregressive video generation enables efficient long-horizon synthesis by repeatedly conditioning on its own outputs. However, this feedback allows small perturbations to a conditioning image to influence generation far beyond the initial frames. We investigate this vulnerability in distilled autoregressive video models. Our analysis links long-horizon sensitivity to local prediction discrepancies and their propagation, motivating corrective supervision during generation. We propose teacher-guided on-policy adversarial fine-tuning to improve distilled models with input robustness while retaining their few-step, closed-loop long-video generation capabilities. A frozen self-teacher provides corrective supervision along the model's own adversarial rollouts, with regularization preserving its original generative behavior. We further design an efficient attack surrogate to sustain frequent adversarial exposure during adaptation. Across long-video evaluations, our approach reduces mean latent perturbation response by 22.3–31.4% relative to the pretrained baseline, with a 14.5% reduction in later frames under attacks specifically targeting late-video generation. VBench-I2V further shows improved quality under attack with near-unchanged clean scores. These results demonstrate that distilled autoregressive generators can learn greater input robustness without additional inference cost.