Fine-tune a search agent with multi-turn RL on Amazon SageMaker AI
TL;DR
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.
Nauti's Take
For small teams, the useful test is a tightly scoped search task with measurable error rates, rather than a broad agent benchmark. First verify that your tools, indexes, and evaluation data are stable enough for multi-turn RL to learn genuine reliability instead of memorizing the quirks of a demo environment.
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