做 LLM 后训练（RFT/RL）的团队终于有了统一的奖励评估框架，不用再为不同任务拼凑规则和检查表了——Skill-RM 用智能体思路动态整合证据，效果还更好，做对齐和强化学习的建议直接看代码。
当前大语言模型后训练中的奖励模型依赖规则验证器、真实参考、程序检查表等异构标准，缺乏统一机制。Skill-RM 将奖励建模重构为可复用的“奖励评估技能”执行过程，通过智能体动态选择和聚合证据，实现一致且透明的评估。在奖励基准和下游任务（如 Best-of-N 选择和强化学习）中，Skill-RM 持续超越传统基线。该方法为奖励建模提供了统一解决方案，并通过策略性证据编排取得更优性能。代码已开源。
Skill-RM: Unifying Heterogeneous Evaluation Criteria via Agent Skill
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.