The progress of AI is bottlenecked by the quality of evaluation, and powerful LLM-as-a-Judge models have proved to be a core solution. Improved judgment ability is enabled by stronger chain-of-thought reasoning, motivating the need to find the best recipes for training such models to think. In this work we introduce J1, a reinforcement learning approach to training such models. Our method converts both verifiable and non-verifiable prompts to judgment tasks with verifiable rewards that incentivize thinking and mitigate judgment bias. In particular, our approach outperforms all other existing 8B or 70B models when trained at those sizes, including models distilled from DeepSeek-R1. J1 also outperforms o1-mini, and even R1 on some benchmarks, despite training a smaller model. We provide analysis and ablations comparing Pairwise-J1 vs Pointwise-J1 models, offline vs online training recipes, reward strategies, seed prompts, and variations in thought length and content. We find that our models make better judgments by learning to outline evaluation criteria, comparing against self-generated reference answers, and re-evaluating the correctness of model responses.
View on arXiv@article{whitehouse2025_2505.10320,
title={ J1: Incentivizing Thinking in LLM-as-a-Judge via Reinforcement Learning },
author={ Chenxi Whitehouse and Tianlu Wang and Ping Yu and Xian Li and Jason Weston and Ilia Kulikov and Swarnadeep Saha },
journal={arXiv preprint arXiv:2505.10320},
year={ 2025 }
}