在 Amazon SageMaker AI 上用知识蒸馏构建高效智能体
原标题：Build efficient agents with knowledge distillation on Amazon SageMaker AI
AI 摘要
AWS Machine Learning Blog 发布教程，介绍如何在 Amazon SageMaker AI 上使用序列级知识蒸馏（SeqKD）构建高效智能体。文章以购物助手为例，说明用前沿教师模型生成多步工具调用轨迹，筛选出成功轨迹后对小型学生模型做监督微调，从而在保持质量的同时降低推理成本。内容涵盖 SeqKD 的适用场景、前置条件以及数据生成、编码、训练和评估流程。
正文节选
Build efficient agents with knowledge distillation on Amazon SageMaker AI You built a shopping assistant on a frontier model, and it works. A shopper asks for “a black waist bag under twenty dollars from a shop with at least 4.5 stars,” and the agent searches the catalog, inspects candidates, checks the constraints, and commits to a recommendation. But what does that interaction actually cost? An agentic assistant is not one model call, it is a loop: search, read results, refine, verify, decide.