JudgeLRM: Large Reasoning Models as a Judge
Authors: name: Nuo Chen, name: Zhiyuan Hu, name: Qingyun Zou, name: Jiaying Wu, name: Qian Wang, name: Bryan Hooi, name: Bingsheng He

The rise of Large Language Models (LLMs) as evaluators offers a scalable
alternative to human annotation, yet existing Supervised Fine-Tuning (SFT) for
judges approaches often fall short in domains requiring complex reasoning. In
this work, we investigate whether LLM judges truly benefit from enhanced
reasoning capabilities. Through a detailed analysis of reasoning requirements
across evaluation tasks, we reveal a negative correlation between SFT
performance gains and the proportion of reasoning-demanding samples -
highlighting the limitations of SFT in such scenarios. To address this, we
introduce JudgeLRM, a family of judgment-oriented LLMs trained using
reinforcement learning (RL) with judge-wise, outcome-driven rewards. JudgeLRM
models consistently outperform both SFT-tuned and state-of-the-art reasoning
models. Notably, JudgeLRM-3B surpasses GPT-4, and JudgeLRM-7B outperforms
DeepSeek-R1 by 2.79% in F1 score, particularly excelling in judge tasks
requiring deep reasoning.