Direct Autoregressive Diffusion Distillation via Error-aware Causal Pretraining

Real-time autoregressive (AR) video diffusion has progressedrapidly. Existing approaches typically distill pretrained bidirectional videofoundation models into few-step causal students; however, naive distilla-tion often collapses due to architectural mismatch. To obtain stable ini-tialization, prior methods rely on ordinary differential equation (ODE)trajectory matching, which incurs substantial computation and can in-troduce errors by forcing the student to imitate global trajectories. Inthis paper, we bypass trajectory matching stage by pretraining a robustcausal AR model that equips the student for direct few-step distillation.Moreover, to bridge the gap between pretraining and distillation, we pro-pose Error Forcing, an error-aware and parallelizable AR video diffu-sion framework. During training, Error Forcing injects controlled residualerrors into the conditioning context, approximating the inference-timehistory distribution. This enables the causal model to learn from de-graded yet clean contexts, improving robustness to accumulated errorswithout sacrificing parallel training throughput. With this initialization,we can perform direct distillation, and the student and teacher/criticnaturally operate under consistent conditioning distributions. Extensiveexperiments show that our method outperforms existing baselines in gen-eration quality during both the pre-training and distillation stages.