JudgeLRM

This Space demonstrates the JudgeLRM model, designed to evaluate the quality of two AI assistant responses. JudgeLRM is 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.

Enter an instruction and two responses, and the model will think, reason and score them on a scale of 1-10 (higher is better).

You can also select Hugging Face models to automatically generate responses for evaluation.
Select Judge Model
Select Model 1
Select Model 2
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Examples
Instruction/Question Select Model 1 Select Model 2
@misc{nuo2025judgelrm, title={JudgeLRM: Large Reasoning Models as a Judge}, author={Nuo Chen, Zhiyuan Hu, Qingyun Zou, Jiaying Wu, Qian Wang, Bryan Hooi, Bingsheng He}, year={2025}, eprint={2504.00050}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2504.00050}, } @misc{wang2025assessingjudgingbias, title = {Assessing Judging Bias in Large Reasoning Models: An Empirical Study}, author = {Qian Wang, Zhanzhi Lou, Zhenheng Tang, Nuo Chen, Xuandong Zhao, Wenxuan Zhang, Dawn Song, Bingsheng He}, year = {2025}, }