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A research paper on training few-step autoregressive text-to-video models in a single stage, reporting a 4-step generator that reaches 84.63 VBench Total from a 1.3B-parameter base. The main takeaways for readers are the proposed alignment and distribution-matching losses and the finding that deeper and intermediate features serve different roles, though the results are benchmark-based and not yet independently validated. Its practical value is in cutting training complexity and latency for streaming video generation.
Direct Forcing: A Single-Stage Training Recipe for Few-Step Autoregressive Text-to-Video Generation
arXiv:2610.30861v1 Announce Type: cross Abstract: Few-step autoregressive video generation enables low-latency streaming, yet prevailing training pipelines separate causal adaptation from distribution matching. Recent representation-based text-to-video methods retain this separation by starting from ODE-initialized checkpoints, even when the target distribution is defined directly by reference videos. We introduce Direct Forcing, a single-stage framework that uses reference videos both as paired examples of how a history continues and as a target population for self-generated rollouts. Causal Continuation Alignment learns paired spatiotemporal correspondence under reference histories, while Rollout Distribution Matching uses maximum mean discrepancy to align the representation distributions of self-generated and reference videos. Together, these complementary signals allow a pretrained video model to learn causal continuation and the target video distribution in a single training stage. Dynamic Gradient Balancing adapts the relative gradient contributions of continuation alignment and distribution matching during training. Starting from bidirectional Wan2.1-1.3B, Direct Forcing learns a four-step autoregressive generator in 250 updates without a separate initialization stage or online diffusion-score supervision, achieving 84.63 VBench Total and 86.01 Quality. Ablation studies further demonstrate the benefits of dynamic gradient balancing and reveal distinct representation preferences: deeper features benefit continuation alignment, whereas intermediate features yield better performance for rollout distribution matching.
Abstract:Few-step autoregressive video generation enables low-latency streaming, yet prevailing training pipelines separate causal adaptation from distribution matching. Recent representation-based text-to-video methods retain this separation by starting from ODE-initialized checkpoints, even when the target distribution is defined directly by reference videos. We introduce Direct Forcing, a single-stage framework that uses reference videos both as paired examples of how a history continues and as a target population for self-generated rollouts. Causal Continuation Alignment learns paired spatiotemporal correspondence under reference histories, while Rollout Distribution Matching uses maximum mean discrepancy to align the representation distributions of self-generated and reference videos. Together, these complementary signals allow a pretrained video model to learn causal continuation and the target video distribution in a single training stage. Dynamic Gradient Balancing adapts the relative gradient contributions of continuation alignment and distribution matching during training. Starting from bidirectional Wan2.1-1.3B, Direct Forcing learns a four-step autoregressive generator in 250 updates without a separate initialization stage or online diffusion-score supervision, achieving 84.63 VBench Total and 86.01 Quality. Ablation studies further demonstrate the benefits of dynamic gradient balancing and reveal distinct representation preferences: deeper features benefit continuation alignment, whereas intermediate features yield better performance for rollout distribution matching.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2610.30861 [cs.CV]
(or arXiv:2610.30861v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.30861 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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