AWS shows how to distill a shopping agent with SeqKD on SageMaker AI
Overview
AWS Machine Learning Blog publishes a guide to sequence-level knowledge distillation (SeqKD) for a shopping agent on Amazon SageMaker AI. SeqKD fine-tunes a smaller student model on the text outputs of a frontier teacher model, so it needs no access to the teacher's weights or logits.
The post covers trajectory generation, supervised fine-tuning, serverless training, and evaluation against a held-out benchmark. The authors present the method as a way to lower the per-task cost of multi-call agent loops while keeping much of the teacher's quality.
Written by AI from the articles below · updated Nov 2, 1:00 PM ET
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- AWS Machine Learning BlogOfficialAmazon shows how to distill a shopping agent with SeqKD on SageMaker AI
AIAWS Machine Learning Blog explains sequence-level knowledge distillation (SeqKD), which fine-tunes a smaller student model on a frontier teacher's text outputs and needs no teacher weights or logits. The post applies it to a shopping agent on Amazon SageMaker AI, covering trajectory generation, supervised fine-tuning, serverless training, and evaluation against a held-out benchmark. The approach aims to cut per-task costs of multi-call agent loops while keeping much of the teacher's quality.
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