On-Policy Adversarial Flow Distillation for Autoregressive Video Generation

Autoregressive video generators are attractive for streaming, long-horizon, and interactive applications, but distilling strong black-box teachers into causal students remains difficult. The student must learn under its own rollout distribution, whereas practical teachers may expose only prompt-conditioned completed videos and may differ in architecture, capacity, temporal design, and sampling schedule. This interface makes supervised fine-tuning off-policy, score-based distillation inapplicable, and direct adversarial imitation too sparse for denoising-time credit assignment. We propose Adversarial Flow Distillation (AFD), an on-policy framework for heterogeneous black-box video distillation. AFD trains a prompt-paired Bradley–Terry discriminator on teacher videos and current student rollouts, then uses its rollout-specific advantage to weight forward-process flow-matching updates on the student's noised states. This provides dense velocity supervision without teacher scores, denoising trajectories, or step alignment. At the population optimum, the discriminator score recovers the teacher through exponential tilting, while its centered linear tilt yields strict local reverse-KL descent. Across two causal AR student families, AFD improves motion- and physics-sensitive generation while preserving general video quality. Independent physics evaluation and feedback ablations further support these gains and the importance of adaptive on-policy feedback. The method requires only clean teacher videos and student rollouts, providing a practical route for distilling proprietary or heterogeneous video generators into efficient autoregressive students.