{
  "id": 1260,
  "url": "https://arxiv.org/abs/2606.09371v1",
  "title": "Capability-Aligned Hierarchical Learning for Tool-Augmented LLMs",
  "summary": "Tool learning enables LLMs to invoke external tools to accomplish tasks. Prior studies have demonstrated the effectiveness of a hierarchical structure: a high-level policy handles global planning and decomposes tasks into manageable sub-tasks, and a low-level policy focuses on invoking tools to solve these sub-tasks. However, these works typically optimize the high-level and low-level policies separately, leading to planner-executor misalignment and limiting LLM performance on tool-use tasks. In",
  "authors": "Haotong Yang, Ting Long, Yi Chang",
  "category": "research",
  "topics": "regulation,safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-08T11:48:55.000Z",
  "fetched_at": "2026-07-14T14:15:07.846Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/1260",
  "original_url": "https://arxiv.org/abs/2606.09371v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}