LLM judges increasingly evaluate responses against fine-grained rubric checklists. When a sample requires multiple rubrics, current methods typically assess each in a separate inference call. Evaluating all rubrics in a single pass is a natural alternative with greater efficiency, but we find that it introduces rubric...
Dingyao Yu, Tong Zhang, Yutao Mou et al.· 0 citations
This work proposes TrustRoboReward, a multi-paradigm reward modeling framework equipped with Preference-Ordered Isotonic Score Editing (POISE), and constructs a unified four-paradigm dataset with trajectory progress scoring, video-QA answer quality scoring, and their pairwise counterparts.
Yi-Dong Wang, Yan Zhan, Ziteng Feng et al.· 0 citations
It is found that OPD transfers a teacher's reasoning behavior rather than its answers to particular problems: training difficulty barely matters, and even problems the teacher never solves are useful.
Zhaoyi Li, Deyang Kong, Yuan Wei et al.· 1 citation
Together, the perplexity analysis indicates improved continuation predictability, while the controlled pre-training experiments suggest that this augmentation can improve model performance without changing the standard pre-training objective.
Haoran Que, Jia-Jun Shi, Ting Huang et al.· 0 citations
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