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该论文提出一种无标签的评分标准（rubric）修复流程：通过跨评审模型的分歧自动定位有争议的评分条款并由编辑模型重写。实验表明该方法提升了评审间一致性（Krippendorff's α 提升约 0.07），但并未提升与人类专家评分的一致性，甚至点估计略低于随机条款编辑对照组。
RewardVerse presents a rubric-based framework for video reward modeling to mitigate scalar drift and provide stable evaluation criteria, enhancing reinforcement learning in video generation.
This paper introduces a rubric-aligned method for evaluating human simultaneous interpreting at the segment level, using a LoRA-adapted COMET-KIWI model to disentangle dimensions like meaning transfer and delivery quality, achieving improved correlation with human ratings over baseline methods.
PaperGym is a framework that converts scientific papers into training environments by separating research questions from evaluation rubrics, enabling reinforcement learning to improve research planning. It demonstrates improved performance over existing methods, with trained models outperforming larger ones on benchmarks.
Cornell researchers propose POP, a self-play framework that lets an LLM generate its own rubrics and training pairs for open-ended tasks, boosting Qwen-2.5-7B on healthcare QA, creative writing and instruction following without human labels.