arXiv cs.AI· Qiming Shi, Yibo Dou, Jiawen Zhu, Yulong Tao, Linbo Jin, Zhaolu Kang, Yunfan Zhou, Di Weng·· 3 天前AI 评分30
SKILL-KD：面向 LLM 智能体的对比技能蒸馏
SKILL-KD: Contrastive Skill Distillation for LLM Agents
AI 导读
SKILL-KD 是一个对比技能蒸馏框架，将技能作为不同能力智能体之间的显式蒸馏介质：给定学生智能体的失败轨迹与教师在同一任务上的轨迹，把二者可操作的差异蒸馏为文本技能补丁，通过重跑学生智能体验证并在仍失败时迭代优化。
正文 · 原文
Abstract:Skill-based prompting has become a practical mechanism for improving LLM agents, yet existing methods often treat skills as summaries of the agent's own experience or of successful demonstrations. This creates a mismatch for weaker student agents. A failed trajectory may not reveal the missing knowledge or strategy, while a teacher trajectory may be too implicit to internalize. We propose SKILL-KD, a contrastive skill distillation framework that treats skills as an explicit distillation medium between agents of different capabilities. Given a student failure and the teacher trajectory on the same task, SKILL-KD distills their actionable discrepancy into a textual skill patch, evaluates it by re-running the student, and iteratively refines it when the student still fails. To prevent skill drift from repeated local updates, SKILL-KD maintains trace-linked edit histories and performs Drift-Aware Skill Consolidation to decide whether each patch is added, merged, or skipped. Across five agent benchmarks and two student settings, SKILL-KD consistently improves frozen student agents over fixed-model adaptation baselines.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.28048 [cs.AI] |
| (or arXiv:2607.28048v4 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.28048 arXiv-issued DOI via DataCite |
Submission history
From: Qiming Shi [view email]
[v1]
Thu, 30 Jul 2026 11:27:37 UTC (694 KB)
[v2]
Tue, 4 Aug 2026 02:41:24 UTC (694 KB)
[v3]
Mon, 5 Oct 2026 11:46:27 UTC (696 KB)
[v4]
Tue, 13 Oct 2026 09:38:38 UTC (698 KB)
来源：arXiv cs.AI · arxiv.org