**OPID: On-Policy Skill Distillation for Agentic Reinforcement Learning** > Out

**OPID: On-Policy Skill Distillation for Agentic Reinforcement Learning**

> Outcome-based reinforcement learning provides a stable optimization backbone for language agents, but its sparse trajectory-level rewards provide little guidance on which intermediate decisions should be reinforced or suppressed. On-policy self-distillation offers dense token-level supervision, yet existing skill-conditioned variants often rely on external skill memories or retrieved privileged context, which are cos...

**来源信息**
- **来源**：HuggingFace Daily Papers（社区热门论文）
- **分类**：行业
- **发布时间**：2026-06-26 08:00（北京时间）
- **原文**：[打开原文](https://huggingface.co/papers/2606.26790)