Unsupervised Multimodal Reward Modeling
Vision-language reward models are trained on annotated preference pairs, which are expensive to collect, can disagree, and are ranked by benchmark accuracy. We build one with no human preference label: a linear head on an intermediate layer of a frozen Qwen3-VL backbone, trained to score a web caption above a perturbed...
Yue-Ying Li, Jing-Xuan Fan, Kianté Brantley et al.
· 0 citations