Reading
UniRRM is the first unified reasoning reward model supporting 103 languages and three evaluation paradigms (pairwise, listwise, pointwise), achieving high-quality evaluation through dynamic rubric generation and two-stage training.
Section 01
UniRRM is the first unified reasoning reward model supporting 103 languages and three evaluation paradigms (pairwise, listwise, pointwise), achieving high-quality evaluation through dynamic rubric generation and two-stage training.
Section 02
Section 03
With the rapid development of large language models (LLMs), accurately evaluating the quality of model-generated responses has become a core challenge. Existing reward models typically have the following limitations:
UniRRM was created to address these issues. As a paper accepted by ICML 2026, it proposes the first unified reasoning reward model that supports 103 languages and three evaluation paradigms simultaneously.
Section 04
UniRRM introduces a phased reasoning chain that dynamically generates task-general and instruction-specific evaluation rubrics. This mechanism enables the model to:
Section 05
This is the most groundbreaking design of UniRRM. Through a unified architecture, the model can handle:
Users can switch evaluation modes simply by adjusting the number of <Response> blocks in the input:
Section 06
UniRRM is trained on the MixReward dataset, which covers:
This allows the model to maintain stable evaluation quality across different languages and cultural backgrounds.
Section 07
UniRRM adopts a carefully designed two-stage training strategy:
Stage 1: Supervised Fine-Tuning (SFT)
Full fine-tuning is performed based on the LLaMA-Factory framework to build basic evaluation capabilities. This stage allows the model to learn how to:
Stage 2: Reinforcement Learning (GRPO)
The verl framework and GRPO (Group Relative Policy Optimization) algorithm are used to further optimize the model's reasoning capabilities. This stage aims to:
Section 08
UniRRM has achieved near-state-of-the-art (SOTA) performance in multiple benchmark tests:
Pairwise Evaluation Benchmarks:
Listwise Evaluation:
Pointwise Evaluation (Unseen During Training):
Notably, even without dedicated optimization for pointwise evaluation during training, UniRRM still demonstrates good generalization ability.
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