{
  "id": "2606.26790",
  "title": "OPID: On-Policy Skill Distillation for Agentic Reinforcement Learning",
  "first_seen": "2026-06-26",
  "published_date": "2026-06-25",
  "observed_dates": [
    "2026-06-26"
  ],
  "score": {
    "novelty": 100,
    "practical_impact": 100,
    "technical_depth": 100,
    "implementation_potential": 89,
    "relevance": 100,
    "community_signal": 100,
    "summary_confidence": 95,
    "overall": 98,
    "weights": {
      "novelty": 0.2,
      "practical_impact": 0.2,
      "technical_depth": 0.15,
      "implementation_potential": 0.15,
      "relevance": 0.15,
      "community_signal": 0.1,
      "summary_confidence": 0.05
    }
  },
  "recommendation": "Read",
  "categories": [
    "critical-first routing",
    "hierarchical skills",
    "on-policy trajectories",
    "outcome-based reinforcement learning",
    "policy optimization",
    "reinforcement learning"
  ],
  "innovation_summary": "OPID: On-Policy Skill Distillation for Agentic Reinforcement Learning: We propose OPID (On-Policy Skill Distillation), a framework that extracts skill supervision directly from completed on-policy trajectories.",
  "why_it_matters": [
    "Overall signal 98/100 driven by novelty 100 and practical impact 100.",
    "Primary categories: critical-first routing, hierarchical skills, on-policy trajectories, outcome-based reinforcement learning, policy optimization, reinforcement learning.",
    "Community signal includes 31 upvote(s) and 1 comment(s), which helps separate durable interest from title-only curiosity."
  ],
  "implementation_angle": [
    "Implementation potential scores 89/100; prioritize adaptation paths for internal agent, evaluation, or platform workflows.",
    "No linked repository is present, so expect more translation work before the ideas are production-ready.",
    "Technical depth scores 100/100, so a quick skim should focus on architecture, data, and evaluation sections before full adoption work."
  ],
  "caveat": "No linked implementation is available yet, which raises integration cost and lowers reproducibility confidence.",
  "links": {
    "hugging_face": "https://huggingface.co/papers/2606.26790",
    "arxiv": "https://arxiv.org/abs/2606.26790"
  }
}