SKILL-KD：面向大语言模型智能体的对比技能蒸馏

摘要

基于技能的提示已成为提升大语言模型（LLM）智能体的一种实用机制，然而现有技能获取方法往往将技能视为经验总结、记忆条目或成功示范的直接摘要。这对于较弱的学生智能体造成了不匹配：当学生因缺乏任务知识或操作策略而失败时，其失败轨迹可能不包含足够的证据来推断缺失行为，而教师轨迹也可能过于隐晦，难以被内化为可复用的指导。我们提出SKILL-KD，一种对比式技能蒸馏框架，将技能视为不同能力智能体之间的显式蒸馏媒介。给定学生在同一任务上的失败轨迹和教师轨迹，SKILL-KD将它们的可操作差异蒸馏为文本技能补丁，通过重新运行学生来评估补丁，并在学生仍失败时迭代改进补丁。为防止重复的局部更新导致技能漂移，SKILL-KD进一步维护轨迹关联的编辑历史，并执行漂移感知的技能整合，决定每个补丁应添加新规则、删除或修改现有规则，还是被跳过。在五个智能体基准和两种学生设置中，SKILL-KD始终能提升冻结的学生智能体，优于固定模型适配基线。

English

Skill-based prompting has become a practical mechanism for improving large language model (LLM) agents, yet existing skill acquisition methods often treat skills as experience summaries, memory entries, or direct summaries of successful demonstrations. This creates a mismatch for weaker student agents: when a student fails because it lacks task knowledge or operational strategy, its failed trajectory may not contain enough evidence to infer the missing behavior, while the teacher trajectory may be too implicit to be internalized as reusable guidance. 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 the patch by re-running the student, and iteratively refines the patch when the student still fails. To prevent repeated local updates from causing skill drift, SKILL-KD further maintains trace-linked edit histories and performs Drift-Aware Skill Consolidation, deciding whether each patch should add a new rule, delete or modify an existing rule, or be skipped. Across five agent benchmarks and two student settings, SKILL-KD consistently improves frozen student agents over fixed-model adaptation baselines.