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 queries the teacher and rolls out the current student on the same prompts, trains a prompt-paired Bradley-Terry discriminator to estimate clean-sample teacher-student discrepancy, and converts the resulting on-policy advantage into forward-process flow-matching updates on the student's own noised states. Thus, AFD provides dense velocity-field supervision while requiring no teacher scores, latents, denoising trajectories, step alignment, or reverse-chain reinforcement learning. Experiments across two causal AR student families show that AFD consistently improves motion- and physics-sensitive generation while preserving general video quality, and ablations validate the importance of adaptive on-policy feedback and forward-process credit assignment. 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.
Streaming Autoregressive Video Generation via Diagonal Distillation
South China University of Technology（南方科技大学）；Westlake University（西湖大学）；Johns Hopkins University（约翰霍普金斯大学）；University of California, Merced（加州大学默塞德分校）；The Chinese University of Hong Kong（香港中文大学）
Adversarial Distribution Matching for Diffusion Distillation Towards Efficient Image and Video Synthesis
Sun Yat-Sen University（中山大学）；ByteDance Seed Vision（字节跳动种子视觉）；Guangdong Provincial Key Laboratory of Information Security Technology（广东省信息安全技术重点实验室）；Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China（教育部机器智能与高级计算重点实验室）；Pazhou Lab (HuangPu), Guangzhou, China（琶洲实验室（黄埔），广州，中国）
Causal-rCM: A Unified Teacher-Forcing and Self-Forcing Open Recipe for Autoregressive Diffusion Distillation in Streaming Video Generation and Interactive World Models
Adversarial Concept Distillation for One-Step Diffusion Personalization
Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Computer Vision Center（计算机视觉中心）；Universitat Autònoma de Barcelona（巴塞罗那自治大学）；VCIP, CS, Nankai University（南开大学计算机学院视觉计算与智能感知实验室）；Program of Computer Science, City University of Hong Kong (Dongguan)（香港城市大学（东莞）计算机科学项目）；City University of Hong Kong（香港城市大学）
