Auto-Rubric: Learning to Extract Generalizable Criteria for Reward Modeling

• URL: http://arxiv.org/abs/2510.17314v1
• Date: Mon, 20 Oct 2025 09:01:37 GMT
• Title: Auto-Rubric: Learning to Extract Generalizable Criteria for Reward Modeling
• Authors: Lipeng Xie, Sen Huang, Zhuo Zhang, Anni Zou, Yunpeng Zhai, Dingchao Ren, Kezun Zhang, Haoyuan Hu, Boyin Liu, Haoran Chen, Zhaoyang Liu, Bolin Ding,
• Abstract summary: Reward models are essential for aligning Large Language Models with human values, yet their development is hampered by costly preference datasets and poor interpretability.<n>We build a training-free framework that infers high-quality, query-specific rubrics using a validation-guided textbfPropose-Evaluate-Revise pipeline.<n>Using just 70 preference pairs (1.5% of the source data), our method also empowers smaller models like Qwen3-8B to outperform specialized, fully-trained counterparts.
• Score: 37.237020102873
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Reward models are essential for aligning Large Language Models (LLMs) with human values, yet their development is hampered by costly preference datasets and poor interpretability. While recent rubric-based approaches offer transparency, they often lack systematic quality control and optimization, creating a trade-off between scalability and reliability. We address these limitations with a novel, training-free framework built on a key assumption: \textit{evaluation rubrics underlying human preferences exhibit significant generalization ability across diverse queries}, a property that enables remarkable data efficiency. Our two-stage approach first infers high-quality, query-specific rubrics using a validation-guided \textbf{Propose-Evaluate-Revise} pipeline. Second, it generalizes these granular rubrics into a compact, non-redundant core set by maximizing an \textbf{information-theoretic coding rate}. The final output is an interpretable, hierarchical "Theme-Tips" rubric set. Extensive experiments demonstrate the framework's exceptional data efficiency and performance. Critically, using just 70 preference pairs (1.5\% of the source data), our method also empowers smaller models like Qwen3-8B to outperform specialized, fully-trained counterparts. This work pioneers a scalable, interpretable, and data-efficient path for reward modeling.

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