{
  "schema_version": "1.0",
  "id": "s13:https://arxiv.org/abs/2607.22529v1",
  "slug": "2607-22529v1-0qt758k",
  "url": "https://feed7.dev/p/2607-22529v1-0qt758k",
  "title": "Skill Self-Play: Pushing the Frontier of LLM Capability with Co-Evolving Skills",
  "why_included": "Skill-SP turns agent skills into units for verifiable self-play: generate tasks, solve them, then update the skill library from execution feedback. The abstract provides no per-benchmark effect sizes.",
  "summary": "**Skill Self-Play** combines a proposer, solver, and dynamic skill controller in a reinforcement-learning loop. Skills constrain each task to a verifiable scenario while dynamic routing expands task variety.",
  "practical_implication": "For agent builders, the reusable pattern is to treat skills as both execution scaffolds and curriculum units: sample a skill, generate a harder task, evaluate execution, then revise the library from observed failures.",
  "agent_context": "**Skill Self-Play** combines a proposer, solver, and dynamic skill controller in a reinforcement-learning loop. Skills constrain each task to a verifiable scenario while dynamic routing expands task variety.\n\nFor agent builders, the reusable pattern is to treat skills as both execution scaffolds and curriculum units: sample a skill, generate a harder task, evaluate execution, then revise the library from observed failures.\n\nThe paper reports gains on **tool-use and reasoning benchmarks**, but the supplied abstract gives no per-benchmark numbers, training costs, or evidence that the loop transfers cleanly to coding-agent workloads.",
  "source": {
    "name": "arXiv",
    "url": "https://arxiv.org/abs/2607.22529v1",
    "published_at": "2026-07-24T17:59:22.000Z"
  },
  "source_class": "blog_post",
  "content_type": "Paper",
  "layer": "agent",
  "domains": [],
  "topics": [
    "skills",
    "tool-use",
    "reasoning"
  ],
  "verification": {
    "status": "needs_review",
    "label": "Needs Review",
    "method": "unverified",
    "verified_at": null
  },
  "uncertainty": [
    "The paper reports gains on **tool-use and reasoning benchmarks**, but the supplied abstract gives no per-benchmark numbers, training costs, or evidence that the loop transfers cleanly to coding-agent workloads."
  ],
  "lifecycle": "Current",
  "published_at": "2026-07-24T17:59:22.000Z",
  "modified_at": "2026-07-24T17:59:22.000Z",
  "supersedes": [],
  "expires_at": null,
  "formats": {
    "html": "https://feed7.dev/p/2607-22529v1-0qt758k",
    "json": "https://feed7.dev/p/2607-22529v1-0qt758k.json",
    "markdown": "https://feed7.dev/p/2607-22529v1-0qt758k.md"
  }
}