| title | Rollout-Marginal Distillation for Long-Horizon Autoregressive Video Generation |
|---|---|
| authors | Chenjian Gao, Zhihao Hu, Jianqi Ma, Jun Zhang, Weidong Zhang, Tianfan Xue |
| date | 2026-09-29 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2609.37925 |
| affiliation | Tencent |
| upvotes | 8 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Autoregressive (AR) video diffusion enables low-latency, streamable video generation, but prediction errors often accumulate over long rollouts. Training the generator on its own rollouts exposes it to these imperfect histories. However, existing video-level distribution matching distillation (DMD) scores the whole rollout jointly. Because a chunk is evaluated together with its past and future, its correction can favor matching artifacts in the surrounding context merely to preserve temporal consistency. To provide a clearer visual-quality signal, we introduce Rollout-Marginal Distillation (RMD). RMD retains the generated history for AR prediction but scores each chunk independently against a chunk teacher, ensuring its quality correction is not compromised by an imperfect temporal context. To compensate for the lack of temporal context in independent chunk scoring, RMD subsequently applies video-level DMD to restore temporal coherence. Extensive experiments demonstrate that RMD maintains high visual quality far beyond its training horizon and outperforms video-level DMD baselines. Code and video results are available at https://cjeen.github.io/RMD |
cite
@misc{gao2026rollout,
title={Rollout-Marginal Distillation for Long-Horizon Autoregressive Video Generation},
author={Chenjian Gao and Zhihao Hu and Jianqi Ma and Jun Zhang and Weidong Zhang and Tianfan Xue},
year={2026},
eprint={2609.37925},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2609.37925}
}