{
  "id": 11472015,
  "title": "Fine-tune a search agent with multi-turn RL on Amazon SageMaker AI",
  "url": "https://urgent.news/2026/10/02/fine-tune-a-search-agent-with-multi-turn-rl-on-amazon-sagemaker-ai",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-02T15:44:20.000Z",
  "source": {
    "name": "AWS Machine Learning",
    "slug": "aws-machine-learning",
    "url": "https://aws.amazon.com/blogs/machine-learning/fine-tune-a-search-agent-with-multi-turn-rl-on-amazon-sagemaker-ai/"
  },
  "original_language": "en",
  "account": "Amazon SageMaker AI offers a solution for fine-tuning large language models (LLMs) to create search agents capable of multi-turn interactions. These agents can autonomously decide on retrieval strategies, refine their approach based on previous retrievals, and optimize decisions across multiple turns. However, traditional methods like supervised fine-tuning (SFT) and single-turn reinforcement learning (RL) fail to capture the interdependent decisions made by search agents. Multi-turn reinforcement learning (MTRL) is the ideal training approach, optimizing the agent's performance over a full multi-turn trajectory while considering environment-specific behavior and output quality. In this post, Amazon SageMaker AI MTRL is used to fine-tune a Qwen3.6-27B model for an enterprise search setting. The search agent relies on two tools: lexical search (BM25) for keyword-based queries and vector search for semantic queries. To train the model, the author utilized two datasets: FRAMES for multi-hop factoid QA synthesis and BRIGHT for reasoning-intensive retrieval tasks. The training setup involved configuring datasets, a reward function, and MTRL job settings. The datasets included multi-hop factoid QA requiring synthesis across multiple Wikipedia articles and reasoning-intensive retrieval across 12 domains. The reward function focused on retrieval quality and was designed to encourage efficient search behavior. The MTRL job configuration allowed for modular agent-environment integration, serverless execution at per-token pricing, and asynchronous rollout with bounded off-policy staleness. By using Amazon SageMaker AI MTRL, the authors were able to create a high-performing, cost-effective search agent with reliable multi-turn behavior.",
  "summary": "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.",
  "key_points": [
    "Amazon SageMaker AI enables fine-tuning LLMs for multi-turn search agents.",
    "Multi-turn reinforcement learning (MTRL) optimizes agent performance across full trajectories.",
    "Qwen3.6-27B model trained on FRAMES and BRIGHT datasets for enterprise search."
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}