On-Policy Self-Distillation for Post-Training Few-Step Autoregressive Video Generators

We propose \method, an on-policy self-distillation paradigm for post-training few-step autoregressive (AR) video diffusion models. Existing few-step AR video generators, often obtained through DMD-style distillation from short-clip video teachers, achieve low latency but suffer from error accumulation and weakened motion dynamics during long rollouts. \method incorporates real long-video data into post-training, rather than relying on short-clip video teachers, to further reduce long-horizon degradation and improve motion dynamics while preserving the original few-step inference path. Specifically, we use the DMD-distilled few-step model for both the student and teacher branches. The student follows the exact inference-time rollout, generating each chunk conditioned on its own previously generated KV cache. The teacher is evaluated at the same student-visited denoising states, but uses a cleaner, AR-consistent temporal cache in which older history is replaced with real-video context. The teacher then provides dense trajectory-level supervision to the student. To maintain autoregressive consistency and prevent the teacher from becoming a fully teacher-forced oracle, both branches share an initial real-video prefix, and the teacher retains its most recent model-generated cache chunk. Experiments on Self-Forcing and LongLive demonstrate improved visual quality and motion dynamics, with higher VBench-Long scores. A user study further shows that \method is preferred over the base models in 66.0% of overall-preference judgments (82.5% excluding ties). Visual results, code, and model checkpoints are available through our anonymous https://anonymous.4open.science/w/OPSD-V-2027/project page, https://anonymous.4open.science/r/OPSD-V-Code-2027/code repository, and https://huggingface.co/anonymousopsd-v/OPSD-VHugging Face repository, respectively.