Reward Modeling as Reasoning
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This work proposes a new class of reward models, called ReasRMs, that reformulate reward modeling as a reasoning task. The authors introduce RM-R1, a family of generative reward models that produce interpretable reasoning traces and rubrics during preference judgments. Instead of relying on scalar scores or shallow generation, RM-R1 models leverage structured reasoning and reinforcement learning to improve both interpretability and performance across benchmarks.
RM-R1 adopts a two-stage training process: (1) distillation of reasoning traces from stronger models, and (2) reinforcement learning with verifiable rewards. The Chain-of-Rubrics (CoR) prompting framework guides the model to either solve reasoning problems or generate evaluation rubrics depending on the task type (reasoning or chat).
On RewardBench, RM-Bench, and RMB, RM-R1 models achieve state-of-the-art or near-SOTA performance, outperforming models like GPT-4o and Llama3.1-405B by up to 13.8% despite using fewer parameters and less data.
Ablation studies show that cold-start RL alone is insufficient; task-type classification and high-quality distillation are key. RM-R1's distilled warm-start training leads to more stable learning and longer, more accurate reasoning traces.
RM-R1 also shows strong generalization across domains and better rubric quality than baseline methods, especially in sensitive contexts like safety and medical judgment. The authors open-sourced six RM-R1 models, training data, and code to support reproducibility.
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