OPSD-V: On-Policy Self-Distillation for Post-Training Few-Step Autoregressive Video Generators

• URL: http://arxiv.org/abs/2607.08766v1
• Date: Thu, 09 Jul 2026 17:59:11 GMT
• Title: OPSD-V: On-Policy Self-Distillation for Post-Training Few-Step Autoregressive Video Generators
• Abstract summary: OPSD-V is an on-policy self-distillation paradigm for post-training few-step autoregressive (AR) video diffusion models.<n>Key idea is to introduce real long-video data as temporal context during training and use it to provide dense trajectory-level supervision.<n>Experiments show consistent improvements in visual quality, motion dynamics, and VBenchLong scores.
• Score: 68.69611976783243
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose OPSD-V, an on-policy self-distillation paradigm for post-training few-step autoregressive (AR) video diffusion models. Existing few-step AR video generators can produce long videos with low latency, but still suffer from error accumulation and weakened motion dynamics during long autoregressive rollout. OPSD-V reduces long-horizon degradation while preserving the original few-step inference path. The key idea is to introduce real long-video data as temporal context during training and use it to provide dense trajectory-level supervision. Specifically, the student follows the exact inference-time rollout, generating each chunk conditioned on its own previously generated KV cache. In parallel, the teacher is evaluated at the same student-visited denoising states, but uses a cleaner AR-consistent temporal cache in which older history can be replaced by real-video context. This provides dense denoising-level corrective targets under on-policy AR cache dynamics, without changing the sampler, number of denoising steps, or inference-time cache mechanism. We apply OPSD-V to representative few-step AR video models, including Self-Forcing and LongLive. Experiments show consistent improvements in visual quality, motion dynamics, and VBenchLong scores. A user study with 10 participants comparing 20 video pairs shows that OPSD-V is preferred over the base models in 66.0% of overall-preference judgments (82.5% excluding ties).

Related papers

• Self Gradient Forcing: Native Long Video Extrapolation [60.00513917489133]
  Self Gradient Forcing restores historical context-gradient gap.<n>Code and models will be released to advance research on autoregressive video generation.
  arXiv  Detail & Related papers  (2026-07-22T16:59:23Z)
• Salt: Self-Consistent Distribution Matching with Cache-Aware Training for Fast Video Generation [27.698320788533405]
  Trajectory-style consistency distillation (DMD) can recover sharp, mode-seeking samples, but its local training signals do not explicitly regularize how denoising updates compose across timesteps.<n>We propose Self-Consistent Distribution Matching Distillation (SC-DMD), which explicitly regularizes the endpoint-consistent composition of consecutive denoising updates.
  arXiv  Detail & Related papers  (2026-04-03T15:43:21Z)
• Self-Forcing++: Towards Minute-Scale High-Quality Video Generation [50.945885467651216]
  Diffusion models have revolutionized image and video generation, achieving unprecedented visual quality.<n>Recent work has explored autoregressive formulations for long video generation, typically by distilling from short-horizon bidirectional teachers.<n>We propose a simple yet effective approach to mitigate quality degradation in long-horizon video generation without requiring supervision from long-video teachers or retraining on long video datasets.
  arXiv  Detail & Related papers  (2025-10-02T17:55:42Z)
• DejaVid: Encoder-Agnostic Learned Temporal Matching for Video Classification [4.973664680272982]
  DejaVid is an encoder-agnostic method that enhances model performance without the need for retraining or altering the architecture.<n>We introduce a new neural network architecture inspired by traditional time series alignment algorithms for this learning task.<n>Our evaluation demonstrates that DejaVid substantially improves the performance of a state-of-the-art large encoder.
  arXiv  Detail & Related papers  (2025-06-14T17:39:03Z)
• Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion [67.94300151774085]
  We introduce Self Forcing, a novel training paradigm for autoregressive video diffusion models.<n>It addresses the longstanding issue of exposure bias, where models trained on ground-truth context must generate sequences conditioned on their own imperfect outputs.
  arXiv  Detail & Related papers  (2025-06-09T17:59:55Z)
• Ca2-VDM: Efficient Autoregressive Video Diffusion Model with Causal Generation and Cache Sharing [66.66090399385304]
  Ca2-VDM is an efficient autoregressive VDM with Causal generation and Cache sharing.<n>For causal generation, it introduces unidirectional feature computation, which ensures that the cache of conditional frames can be precomputed in previous autoregression steps.<n>For cache sharing, it shares the cache across all denoising steps to avoid the huge cache storage cost.
  arXiv  Detail & Related papers  (2024-11-25T13:33:41Z)
• ARVideo: Autoregressive Pretraining for Self-Supervised Video Representation Learning [29.620990627792906]
  This paper presents a new self-supervised video representation learning framework, ARVideo, which autoregressively predicts the next video token in a tailored sequence order. Extensive experiments establish ARVideo as an effective paradigm for self-supervised video representation learning.
  arXiv  Detail & Related papers  (2024-05-24T02:29:03Z)
• Spatiotemporal Contrastive Video Representation Learning [87.56145031149869]
  We present a self-supervised Contrastive Video Representation Learning (CVRL) method to learn visual representations from unlabeled videos. Our representations are learned using a contrasttemporalive loss, where two augmented clips from the same short video are pulled together in the embedding space. We study what makes for good data augmentations for video self-supervised learning and find that both spatial and temporal information are crucial.
  arXiv  Detail & Related papers  (2020-08-09T19:58:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.