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Aligning Human Sense: Calibrated Distributional Reward Learning for Video Generation
Unknown
video generationreward modelingpreference alignment
2608.21425
Builder Relevance
2h ago80%
Abstract
This paper proposes a unified preference-aware learning framework to improve video generation by aligning generated content with human preferences.
Reality Card
Core Claim
The proposed framework improves the reliability of reward signals and perceptual consistency of generated videos by addressing challenges in human preference data and policy optimization.
Method / Result
The approach demonstrates improved reliability of reward signals and perceptual consistency in generated videos through elite-guided filtering and Wasserstein-based distributional alignment.
Limitations
The quality of human preference data remains a challenge due to subjective noise and bias.
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