AWS published a guide to fine-tuning a search agent with multi-turn reinforcement learning on Amazon SageMaker AI. The method trains the agent across a whole sequence of searches, using the quality of its final results as the reward.
In AWS’s evaluation, the fine-tuned Qwen3.6-27B improved on three of four held-out benchmarks. On BrowseComp-Plus, its nDCG@10 score rose by 23.7% and its failure rate fell from 22.89% to 0.68%. AWS says the training used one epoch and a batch size of 128; the post does not give a complete set of results in the supplied text.
Why it matters
For teams building search agents on SageMaker AI, AWS’s results suggest multi-turn fine-tuning may reduce task failures, though the supplied post excerpt does not include full benchmark results or training costs.
