{
  "id": 13498,
  "url": "https://arxiv.org/abs/2607.21550v1",
  "title": "X$^3$-OPD: Distilling Reasoning into Large Audio-Language Models via On-Policy Alignment",
  "summary": "While large audio-language models have achieved remarkable progress in auditory perception, they still lag behind text-based large language models in deep logical reasoning, primarily due to the scarcity of high-quality audio reasoning data. To bridge this gap, we propose X$^3$-OPD, a cross-modal on-policy distillation framework that transfers reasoning capabilities from a powerful text teacher to an audio-language student. During training, the student generates reasoning trajectories conditione",
  "authors": "Dongjie Fu, Di Cao, Xize Cheng, Zihan Zhang, Wenxu Jia, Yifu Chen et al.",
  "category": "research",
  "topics": "regulation,safety-alignment,children-education,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-23T17:35:20.000Z",
  "fetched_at": "2026-07-25T05:10:48.796Z",
  "source_slug": "arxiv-cslg",
  "source_name": "arXiv cs.LG",
  "source_homepage": "https://arxiv.org/list/cs.LG/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/13498",
  "original_url": "https://arxiv.org/abs/2607.21550v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}