# AWS shows how to cut agent costs with knowledge distillation on SageMaker

Digest AI · Agents & Tools · published 2026-11-02T17:01:31Z

Canonical: https://digestai.news/story/aws-shows-how-to-cut-agent-costs-with-knowledge-distillation-on-sagema

## Summary

Amazon SageMaker AI published a guide on using **sequence-level knowledge distillation (SeqKD)** to train smaller models that mimic the behavior of larger, more expensive frontier models like **GLM-5.3** on Amazon Bedrock. The method generates high-quality multi-step agent trajectories—complete with reasoning and tool calls—using a teacher model, filters them to keep only task-solving runs, and fine-tunes a smaller student model (e.g., **Qwen3.5-4B**) on those trajectories. The student retains most of the teacher’s task quality while running on cheaper, faster hardware like an **ml.g5.2xlarge** instance.

The process involves encoding trajectories as chat-formatted messages, running serverless supervised fine-tuning on SageMaker AI model customization, and evaluating the student against a held-out benchmark (**ShoppingBench**). Results show the 4B student matched the teacher’s **Absolute Success Rate (ASR)** of **0.541** on 324 held-out problems, with a slight edge over the sampled teacher’s **0.511**. The student also preserved general instruction-following ability, though with a small drop (e.g., **0.741** vs. **0.819** on IFEval’s prompt-strict score). The guide emphasizes honest evaluation—comparing against both the teacher and independent frontier models—and highlights tradeoffs like task capacity limits and the need for on-policy methods for harder problems.

## Key points

- SeqKD trains a smaller model (e.g., **Qwen3.5-4B**) on trajectories generated by a frontier teacher like **GLM-5.3** on Bedrock
- Filtered 850 trajectories from 426 training problems, keeping only task-solving runs scored by **ShoppingBench**’s programmatic grader
- Student matched teacher’s **ASR of 0.541** on 324 held-out problems, running on **ml.g5.2xlarge** instances for lower cost

## Why it matters

SeqKD lets developers cut agent costs by shifting from API-based frontier models to hosted, smaller students without sacrificing task quality. This matters for production agents where per-task API fees add up, especially for tasks like shopping assistants where multi-step reasoning is critical.

## Sources

1. [Build efficient agents with knowledge distillation on Amazon SageMaker AI](https://aws.amazon.com/blogs/machine-learning/build-efficient-agents-with-knowledge-distillation-on-amazon-sagemaker-ai) (AWS Machine Learning Blog, 2026-11-02, primary source)

## Cite

Digest AI, "AWS shows how to cut agent costs with knowledge distillation on SageMaker", 2 November 2026, https://digestai.news/story/aws-shows-how-to-cut-agent-costs-with-knowledge-distillation-on-sagema

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