Ming Li, Jie Wu, Justin Cui, Xiaojie Li, Rui Wang, Chen Chen

Researchers introduced Poly-DPO, an algorithmic extension to preference optimization that adds a polynomial term to handle noisy preference data, and ViPO, a large-scale dataset of 1M image pairs and 300K video pairs with balanced distributions and high-quality signals. The work addresses a core bottleneck in scaling visual generative models: existing preference datasets contain conflicting patterns and quality issues that prevent effective learning. When applied to high-quality data, Poly-DPO converges to standard DPO, validating both the dataset quality and the algorithm's adaptive design.