title: JudgeLRM: Large Reasoning Models as a Judge
summary: 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.
paper_authors._id: 67ec9bf3e58745dc7d652587
paper_authors.name: Nuo Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-03T08:28:54.107Z
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paper_authors.fullname: Nuo Chen
paper_authors.type: user
paper_authors.user: nuojohnchen
paper_authors._id: 67ec9bf3e58745dc7d652588
paper_authors.name: Zhiyuan Hu
paper_authors.status: admin_assigned
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paper_authors._id: 64351475901c5734bcb64248
paper_authors.avatarUrl: /avatars/12346d4301c1bfb00ce0ea128a93cc15.svg
paper_authors.fullname: Zhiyuan Hu
paper_authors.type: user
paper_authors.user: zhiyuanhucs
paper_authors._id: 67ec9bf3e58745dc7d652589
paper_authors.name: Qingyun Zou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-02T09:44:49.663Z
paper_authors._id: 6730a1fed66bf1b6378cd451
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paper_authors.fullname: qy
paper_authors.type: user
paper_authors.user: qingyun777yes
paper_authors._id: 67ec9bf3e58745dc7d65258a
paper_authors.name: Jiaying Wu
paper_authors._id: 67ec9bf3e58745dc7d65258b
paper_authors.name: Qian Wang
paper_authors._id: 67ec9bf3e58745dc7d65258c
paper_authors.name: Bryan Hooi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-02T09:45:24.844Z
paper_authors._id: 651d8032c50012d33e914f2f
paper_authors.avatarUrl: /avatars/0a44c9f51fc50ce86582e328c361ea00.svg
paper_authors.fullname: Bryan Hooi
paper_authors.type: user
paper_authors.user: bhooi
paper_authors._id: 67ec9bf3e58745dc7d65258d
paper_authors.name: Bingsheng He
paper_title: JudgeLRM: Large Reasoning Models as a Judge
paper_summary: 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.
paper_ai_summary: JudgeLRM models trained with reinforcement learning outperform existing models, especially in tasks requiring deep reasoning.
paper_ai_keywords: Large Language Models Supervised Fine-Tuning reinforcement learning judge-wise outcome-driven rewards F1 score DeepSeek-R1