Aligning Human Sense: Calibrated Distributional Reward Learning for Video Generation
Video generation is central to AI-powered content creation. Aligning generated videos with human preferences is a key criterion for evaluating generation quality. Despite significant progress in visual quality, three key challenges remain. First, the reliability of reward signals is constrained by the quality of human preference data, which is often affected by subjective noise and bias. Second, standard scalar reward models collapse multi-aspect human preferences into a single value, leading to the loss of dynamic trade-offs across multiple preference dimensions. Third, in policy optimization, the widely adopted KL divergence imposes primarily local constraints and may fail to capture th...
| Authors | Nai-Xin Zhai, Weihua Cheng, Dexu Yu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 16 August 2026 |
| Last updated at source | 16 August 2026 |
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