{
  "id": 13600,
  "url": "https://arxiv.org/abs/2607.22529",
  "title": "Skill Self-Play: Pushing the Frontier of LLM Capability with Co-Evolving Skills",
  "summary": "LLM training is shifting from manual design and annotation to interaction-driven self-evolution. However, existing self-evolutionary methods face a fundamental dilemma between task diversity and verification reliability: environment-bound methods obtain precise feedback but confine learning to narrow domains, while open-ended self-generation broadens the task space but lacks reliable verification, allowing misleading rewards to pollute the training loop. We identify agent skills as a powerful mi",
  "authors": "Siyuan Huang, Pengyu Cheng, Haotian Liu, Tao Chen, Yihao Liu, Jingwei Ni",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-23T20:00:00.000Z",
  "fetched_at": "2026-07-27T05:10:06.638Z",
  "source_slug": "hf-daily",
  "source_name": "HuggingFace Daily Papers",
  "source_homepage": "https://huggingface.co/papers",
  "ethics_ai_record_url": "https://ethics.ai/record/13600",
  "original_url": "https://arxiv.org/abs/2607.22529",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}