TBNAI
Rollout-Marginal Distillation for Long-Horizon Autoregressive Video Generation
Publication: Hugging Face Daily Papers
Author: Not available in collected metadata
Source published: 28/09/2026, 20:00:00 UTC
Briefing created: 07/10/2026, 01:43:43 UTC
Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 05/10/2026, 12:18:24 UTC.
Briefings
Rollout-Marginal Distillation (RMD) addresses temporal context bias in autoregressive video generation by scoring video chunks independently rather than jointly. This method preserves high visual quality beyond the training horizon and outperforms existing video-level distribution matching distillation baselines. The approach retains generated history for AR prediction while decoupling chunk evaluation from imperfect temporal context, followed by a final video-level DMD step to restore coherence.
Key points
- RMD scores video chunks independently against a chunk teacher to avoid artifacts from imperfect temporal context.
- RMD maintains high visual quality far beyond its training horizon and outperforms video-level DMD baselines.
Context and limitations
The study covers a limited period and snapshot, with specific limitations noted in the source material.
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