Build efficient agents with knowledge distillation on Amazon SageMaker AI

原文摘要 · 中英对照

Agentic assistants built on frontier models run a multi-step tool loop, and paying frontier prices on every step is hard to sustain at production scale. This post shows how to use sequence-level knowledge distillation (SeqKD) to transfer a frontier teacher's agentic behavior into a small, lower-cost student on Amazon SageMaker AI.

基于前沿模型构建的智能体助手会运行多步工具循环，在生产规模下每一步都支付前沿模型价格难以持续。本文展示如何使用序列级知识蒸馏（SeqKD）将前沿教师模型的智能体行为迁移到 Amazon SageMaker AI 上更小、成本更低的学生模型。