Context-Matched Distillation: Teacher Causality for Autoregressive Video Distillation
Interactive autoregressive video generation demands both low-latency rollouts and precise online control. Few-step distillation accelerates generation by reducing denoising steps, while online control imposes a causal constraint: frames and blocks should depend on history and controls available during generation. Existing video distribution matching distillation (DMD) pipelines, however, often supervise causal few-step students using bidirectional teachers that score complete clips. The score for a target can therefore depend on future frames and controls that were unavailable when the student
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- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- FuzzySimilar title/name (fuzzy) · 59%Developer-Y/cs-video-courses →
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- LinkedLinked via arxiv author · 85%Hmrishav Bandyopadhyay →
“Context-Matched Distillation: Teacher Causality for Autoregressive Video Distillation”
- LinkedLinked via arxiv author · 85%Xuanchi Ren →
“Context-Matched Distillation: Teacher Causality for Autoregressive Video Distillation”
- LinkedLinked via arxiv author · 85%Zijian Huang →
“Context-Matched Distillation: Teacher Causality for Autoregressive Video Distillation”
- LinkedLinked via arxiv author · 85%Jay Zhangjie Wu →
“Context-Matched Distillation: Teacher Causality for Autoregressive Video Distillation”
- LinkedLinked via arxiv author · 85%Tianshi Cao →
“Context-Matched Distillation: Teacher Causality for Autoregressive Video Distillation”