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Official Source· AWS Artificial Intelligence· AI· Published Oct 2, 2026

AWS demonstrates multi-turn RL fine-tuning for a search agent on SageMaker AI

AWS has shared a case study fine-tuning a Qwen3.6-27B search agent with multi-turn reinforcement learning (The MTRL) on Amazon SageMaker AI. The agent uses BM25 and vector search tools and chooses what to search, which retrieval strategy to use, and when to stop across multiple turns.

AWS demonstrates multi-turn RL fine-tuning for a search agent on SageMaker AI

Full Report

In a technical post published October 2, 2026, AWS describes fine-tuning an LLM-powered search agent with multi-turn reinforcement learning (The MTRL) on Amazon SageMaker AI. The example uses Qwen3.6-27B. Across multiple interactions, the agent decides what to query, which retrieval strategy to use, and when to stop, refining its approach as it works through a task.

The example enterprise search agent has BM25 lexical search and vector search tools. AWS says The MTRL uses multi-turn rollouts to generate training data and provides a modular agent-environment interface for custom rewards, tool loops, and conversation shapes. The service runs serverlessly at per-token pricing, without requiring users to provision or manage GPU clusters.

AWS outlined The MTRL’s broader agent-training framework in a July 2026 overview; this newer post applies it to a search task. After training, The MTRL evaluation jobs can report reward, pass@k, and trajectory metrics before deployment to an Amazon SageMaker AI endpoint or Amazon Bedrock. The post says Qwen3.6-27B was supported in the US West (Oregon) AWS Region, us-west-2.

Why This Matters

The example makes the multi-turn search use case concrete by connecting the agent’s retrieval tools and training setup with an evaluation-to-deployment path. It also distinguishes this workflow from fine-tuning that only targets single-turn responses.

Evidence

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Primary Evidence

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  • OfficialAWS Artificial IntelligenceDocumentOriginal

    Fine-tune a search agent with multi-turn RL on Amazon SageMaker AI

    Huibin Shen

    “Fine-tuning teaches a small search agent your tools and environment, giving it the reliability of a frontier model at lower latency and cost. In this post, we fine-tune an LLM-powered search agent with multi-turn reinforcement learning (MTRL) on Amazon SageMaker AI and share the gains we measured in retrieval quality and reliability.”

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