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 fo
Record details
Published: 12 August 2026
Source: HuggingFace Daily Papers
Category: Research
Topics: Children & education
Retrieved: 15 August 2026
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ethics.ai (12 August 2026), “Context-Matched Distillation: Teacher Causality for Autoregressive Video Distillation,” evidence record 19503, https://ethics.ai/record/19503 (originally published by HuggingFace Daily Papers).
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