{
  "id": 5870,
  "url": "https://arxiv.org/abs/2605.04078v2",
  "title": "Validity-Calibrated Reasoning Distillation",
  "summary": "Reasoning distillation aims to transfer multi-step reasoning capabilities from large language models to smaller, more efficient ones. While recent methods have shown promising gains, they typically rely on static teacher-student hierarchies and frame distillation as trajectory imitation. This is misaligned with the structure of reasoning, where intermediate steps are often locally under-specified: global correctness constrains the final answer, but does not uniquely determine each intermediate m",
  "authors": "Khouloud Saadi, Di Wang",
  "category": "research",
  "topics": "safety-alignment,children-education",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-14T12:32:12.000Z",
  "fetched_at": "2026-07-14T16:32:06.466Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5870",
  "original_url": "https://arxiv.org/abs/2605.04078v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}