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AWS details building efficient agents with sequence-level knowledge distillation on Amazon SageMaker AI

Learn how to use sequence-level knowledge distillation on Amazon SageMaker AI to fine-tune a smaller agent on teacher trajectories.

AWS details building efficient agents with sequence-level knowledge distillation on Amazon SageMaker AI — image from AWS
Image: AWS

Summary

An article on Amazon Web Services details how to use sequence-level knowledge distillation (SeqKD) to fine-tune a smaller model on multi-step agentic trajectories generated by a frontier teacher. The guide walks through preparing a dataset, running serverless supervised fine-tuning on Amazon SageMaker AI, and deploying the model with LoRA adapters.

What changed

  • Build a shopping assistant agent using knowledge distillation on Amazon SageMaker AI.
  • Generate multi-step trajectories with a frontier teacher model and filter solved runs with a scorer.
  • Fine-tune a student model with serverless supervised fine-tuning using Amazon SageMaker AI model customization.
  • Deploy the base model and student adapter as inference components on a single real-time endpoint.

Availability

Available now

“In this post you will learn how to use knowledge distillation, and specifically sequence-level knowledge distillation (SeqKD), to fine-tune a shopping agent on Amazon SageMaker AI .”

Founder takeaway

Review the multi-step agent distillation guide to learn how to transfer frontier model reasoning to smaller models for lower production inference costs.

Source

AWSBuild efficient agents with knowledge distillation on Amazon SageMaker AI | Amazon Web Services

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