AWS Details Sequence-Level Distillation on SageMaker AI for Agentic Workflows
Frontier AI models · AI assistants · Enterprise AI
AWS outlines a pipeline on Amazon SageMaker AI using sequence-level knowledge distillation (SeqKD) to transfer multi-step reasoning from frontier teacher models to smaller students, lowering per-task inference costs.
Deploying agentic assistants on frontier models often incurs compounding costs across multi-step execution loops. AWS describes an end-to-end workflow utilizing sequence-level knowledge distillation (SeqKD) on Amazon SageMaker AI to train smaller models using text trajectories from larger teachers. The procedure involves sampling multi-turn trajectories with reasoning traces from a frontier teacher, filtering successful runs with programmatic benchmark scorers, and formatting steps for supervised fine-tuning.
Key takeaways
- Agentic tasks incur recurring API costs because each completed workflow consists of an iterative loop of reasoning, tool invocation, and decision-making.
- SeqKD requires only text outputs rather than internal model weights or logits, enabling distillation directly from closed or API-only frontier teachers.
- The walkthrough samples trajectories from GLM-5.3 on Amazon Bedrock, applies rejection sampling via ShoppingBench, and trains a Qwen3.5-4B student on SageMaker AI model customization.
- Training rows pair accumulated conversational context with the subsequent assistant step, allowing loss calculation strictly on reasoning blocks and tool calls.
Source：AWS Machine Learning Read the original ↗
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