{"version":1,"type":"story","url":"https://digestai.news/story/aws-shows-how-to-cut-agent-costs-with-knowledge-distillation-on-sagema","json":"https://digestai.news/story/aws-shows-how-to-cut-agent-costs-with-knowledge-distillation-on-sagema.json","markdown":"https://digestai.news/story/aws-shows-how-to-cut-agent-costs-with-knowledge-distillation-on-sagema.md","slug":"aws-shows-how-to-cut-agent-costs-with-knowledge-distillation-on-sagema","headline":"AWS shows how to cut agent costs with knowledge distillation on SageMaker","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.\n\nThe 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.","keyPoints":["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"],"whyItMatters":"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.","category":{"slug":"agents","name":"Agents & Tools","url":"https://digestai.news/category/agents"},"entities":{"companies":["Amazon","Amazon SageMaker","Amazon Bedrock"],"models":["GLM-5.3","Qwen3.5-4B","ShoppingBench"],"people":[]},"firstPublishedAt":"2026-11-02T17:01:31Z","updatedAt":"2026-11-02T17:01:31Z","sourceCount":1,"hasPrimarySource":true,"sources":[{"outlet":"AWS Machine Learning Blog","title":"Build efficient agents with knowledge distillation on Amazon SageMaker AI","url":"https://aws.amazon.com/blogs/machine-learning/build-efficient-agents-with-knowledge-distillation-on-amazon-sagemaker-ai","publishedAt":"2026-11-02T17:01:31Z","type":"primary","primary":true,"lead":true}],"sourceNotes":null,"discussions":[],"thread":null,"cite":{"text":"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","publisher":"Digest AI","title":"AWS shows how to cut agent costs with knowledge distillation on SageMaker","datePublished":"2026-11-02T17:01:31Z","url":"https://digestai.news/story/aws-shows-how-to-cut-agent-costs-with-knowledge-distillation-on-sagema"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}