Few-step autoregressive video diffusion enables efficient generation, yet maintaining consistency over long rollouts remains challenging, especially when subjects or scenes reappear after going out of
History Forcing improves long-range consistency in few-step autoregressive video generation by using historical context residuals to correct distillation targets.
Few-step autoregressive video diffusion enables efficient generation, yet maintaining consistency over long rollouts remains challenging, especially when subjects or scenes reappear after going out of sight. Despite progress in fidelity and rollout robustness, existing distillation methods do not fully exploit richer historical information to constrain the student's current prediction. To address this gap, we introduce History Forcing, a unified on-policy distillation framework that strengthens historical supervision for few-step autoregressive video generators. The framework uses the residual between teacher predictions with and without informative historical context to decouple this…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
History Forcing: Distilling History-Dependent Continuations for Long-Horizon Video World Models was first seen on Hugging Face · Daily papers on Nov 4, 2026. Since then it has appeared in 9 checks on 1 different day. Its best position was #14 of 30, reached on Nov 4, 05:28 UTC. It dropped off the list after Nov 4, 19:28 UTC.
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It was first seen on Nov 4, 2026 and has been on the list on 1 day (9 checks) since.