A Systematic Post-Train Framework for Video Generation
While large-scale video diffusion models have demonstrated impressive capabilities in generating high-resolution and semantically rich content, a significant gap remains between their pretraining performance and real-world deployment requirements due to critical issues such as prompt sensitivity, temporal inconsistency, and prohibitive inference costs. To bridge this gap, we propose a comprehensive post-training framework that systematically aligns pretrained models with user intentions through four synergistic stages: we first employ Supervised Fine-Tuning (SFT) to transform the base model into a stable instruction-following policy, followed by a Reinforcement Learning from Human Feedbac...
| Authors | Zeyue Xue, Siming Fu, Jie Huang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 April 2026 |
| Last updated at source | 28 April 2026 |
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