AWS Blog Shows Knowledge Distillation for Shopping Agents on SageMaker AI

What happened

An AWS Machine Learning Blog post explains how to use sequence-level knowledge distillation (SeqKD) to fine-tune a shopping agent on Amazon SageMaker AI, training a smaller student model on multi-step trajectories generated by a frontier teacher. The post covers generating trajectories, keeping only those that solve the task, encoding them for supervised fine-tuning, running training on serverless infrastructure, and evaluating the student against a held-out benchmark.

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The story so far

An AWS Machine Learning Blog post describes a method for fine-tuning a shopping agent on Amazon SageMaker AI using sequence-level knowledge distillation (SeqKD): a smaller student model is trained on multi-step trajectories generated by a frontier teacher model. The post walks through generating those trajectories, keeping only the ones that solve the task, encoding them for supervised fine-tuning, running training on serverless infrastructure, and evaluating the student against a held-out benchmark. The post presents this as a how-to rather than reporting measured results, so it does not establish how well the distilled student performs relative to the teacher.