Paper Detail
Tao Chen, Gangwei Jiang, Pengyu Cheng, Siyuan Huang, Yihao Liu, Jingwei Ni, Jiaqi Guo, Mengyu Zhou, Kai Tang, Junling Liu, Qinliang Su, Xiaoxi Jiang, Guanjun Jiang
Reward models (RMs) provide critical feedback signals for LLM post-training, notably in reinforced fine-tuning (RFT) and reinforcement learning (RL) pipelines. However, current reward evaluation relies on heterogeneous criteria such as rule-based verifiers, ground-truth references, procedural checklists, and complex rubrics, where a unified mechanism to integrate all types of evidence remains unexplored. To this end, we propose Skill Reward Model (Skill-RM), a unified framework that reformulates reward modeling as the execution of a reusable Reward-Evaluation Skill. By treating reward computation as a structured agentic task, Skill-RM provides a consistent interface to orchestrate heterogeneous resources, dynamically selecting and aggregating evidence tailored to the specific requirements of each input. This approach enables the reward model to move beyond static evaluation, ensuring consistency and transparency across diverse tasks. Extensive experiments on reward benchmarks and downstream applications, including best-of-N selection and reinforcement learning, demonstrate that Skill-RM consistently outperforms traditional judge baselines. Our findings suggest that Skill-RM not only provides a unified solution for reward modeling but also achieves superior performance through the strategic and dynamic orchestration of evidence. The code is at https://github.com/Qwen-Applications/Skill-RM.
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@misc{chen2026skill,
title = {Skill-RM: Unifying Heterogeneous Evaluation Criteria via Agent Skill},
author = {Tao Chen and Gangwei Jiang and Pengyu Cheng and Siyuan Huang and Yihao Liu and Jingwei Ni and Jiaqi Guo and Mengyu Zhou and Kai Tang and Junling Liu and Qinliang Su and Xiaoxi Jiang and Guanjun Jiang},
year = {2026},
abstract = {Reward models (RMs) provide critical feedback signals for LLM post-training, notably in reinforced fine-tuning (RFT) and reinforcement learning (RL) pipelines. However, current reward evaluation relies on heterogeneous criteria such as rule-based verifiers, ground-truth references, procedural checklists, and complex rubrics, where a unified mechanism to integrate all types of evidence remains unexplored. To this end, we propose Skill Reward Model (Skill-RM), a unified framework that reformulates},
url = {https://huggingface.co/papers/2606.03980},
keywords = {reward models, reinforced fine-tuning, reinforcement learning, reward evaluation skill, heterogeneous criteria, evidence aggregation, structured agentic task, reward modeling, code available, huggingface daily},
eprint = {2606.03980},
archiveprefix = {arXiv},
}
{}