Skill-RM: Unifying Heterogeneous Evaluation Criteria via Agent Skill 事件

Skill-RM: Unifying Heterogeneous Evaluation Criteria via Agent Skill arXiv:2606.03980v1 Announce Type: cross 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 e