Build efficient agents with knowledge distillation on Amazon SageMaker AI

Amazon's guide shows how to cut the cost of agentic assistants by distilling a frontier teacher model into a smaller student on Amazon SageMaker AI.

• An agentic assistant is not one model call but a loop of search, read, refine, verify and decide, and each step is a separate billed call to the frontier model, so per-task cost compounds at production scale.
• The post walks through sequence-level knowledge distillation (SeqKD), which trains a student on the teacher's own text outputs with supervised fine-tuning and needs no teacher logits or reinforcement-learning infrastructure.
• Because SeqKD only needs the teacher's text, it works with a closed or API-only teacher, unlike logit-based or on-policy methods that require internal access.
• The guide covers generating multi-step trajectories with a frontier teacher, keeping only the runs 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 cites the DeepSeek-R1 distilled models, whose teacher reasoning was distilled into smaller Qwen and Llama students, as a well-known example of the approach.