🔥 DistillAlign: Coordinating Mode Covering and Mode Seeking in Autoregressive Video Distillation

💡 This paper addresses the issue of existing autoregressive video distillation methods which typically decouple the initialization and distribution matching stages, leading to suboptimal results. The authors argue that a good initialization should match the mode coverage of the target distribution, rather than merely pursuing high quality. To analyze this, they introduce a distributional evaluation protocol that measures precision and coverage between student and teacher distributions in a shared latent space.

The authors find that some initializations reach high precision but low coverage, leading to suboptimal refinement, while mode-covering ones preserve broader support. Furthermore, even when the target distributions are aligned, the reverse-KL objective of distribution matching can still drive the student towards high-probability teacher regions in late training, reducing coverage and diversity.

To address this, the authors propose joint distillation, which combines the mode-seeking objective of distribution matching with a consistency distillation-based mode-covering constraint. The experiments show that their method improves generation quality, coverage, and diversity. Notably, even with a smaller teacher model, their method outperforms baselines refined with a larger teacher model, underscoring the importance of distributional alignment in autoregressive video distillation.

The main contributions of this paper are the introduction of a distributional evaluation protocol and the proposal of joint distillation, which coordinates mode covering and mode seeking in autoregressive video distillation. The results demonstrate the effectiveness of the proposed method in improving the quality and diversity of generated videos.

📅 Published on Jul 29

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.26811
• PDF: https://arxiv.org/pdf/2607.26811
• Project Page: https://lijiaxing0213.github.io/DistillAlign/

📊 Datasets citing this paper:
• https://huggingface.co/datasets/LiJiaxing/DistillAlign_1p3b_25K

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📢 By: https://t.iss.one/PaperNexus

#AutoregressiveVideoDistillation #ModeCovering #DistributionMatching #VideoDistillationMethods #LatentSpaceAnalysis