AWS shows how to fine-tune a smaller shopping agent on trajectories generated by a frontier teacher model.

AWS published a walkthrough for building efficient agents with knowledge distillation on Amazon SageMaker AI. The approach, sequence-level knowledge distillation, trains a smaller student model on responses that a larger teacher model generates. The student then handles multi-step tasks at lower latency and lower cost.

An agentic assistant runs a loop of search, result reading, refinement, verification, and decision, and each step bills as a separate call to the frontier model. Those per-task costs compound at production scale. Sequence-level distillation avoids logit access and reinforcement-learning infrastructure, which keeps the method portable to closed or API-only teachers.

AWS advises teams to generate multi-step trajectories with the teacher, keep only the ones that solve the task, encode them for supervised fine-tuning, and run training on serverless infrastructure. Distilled datasets persist, so operators can reuse the same trajectories to train or test different student models without regenerating data.

The remaining work sits in evaluation. Teams must measure the student against a held-out benchmark to confirm the quality gap stays small enough for the task. AWS frames the choice as a trade between speed and cost on one side and answer quality on the other, and the benchmark decides whether a model swap is safe.

What matters

• AWS published a guide for training smaller agents on trajectories that a frontier teacher model generates.
• Sequence-level distillation needs only teacher text, so closed and API-only models can serve as teachers.
• Teams should compare student quality against a held-out benchmark before swapping out the frontier model.

Why it matters

Teams should compare student quality against a held-out benchmark before swapping out the frontier model.

This GenAI News article was prepared in original wording using reporting and materials published by AWS Machine Learning Blog. Source reference: https://aws.amazon.com/blogs/machine-learning/build-efficient-agents-with-knowledge-distillation-on-amazon-sagemaker-ai/.

Drafted by the GenAI News review pipeline.