SageMaker multi-turn RL: what a search team must verify before training
AWS reports gains on three of four held-out search benchmarks for its Qwen3.6-27B agent. This guide maps the SageMaker setup, costs and evaluation needed before a trial.
The big change
In its October 2 walkthrough, AWS describes fine-tuning a Qwen3.6-27B search agent with multi-turn reinforcement learning (MTRL) in SageMaker AI. The chart above plots AWS's reported held-out nDCG@10 scores: they rose on WixQA, Wands and BrowseComp-Plus, and fell slightly on FreshStack. BIG CHANGE made the chart from AWS's figures; we did not independently test the agent or reproduce the results. The technique trains a model against the outcome of a sequence of tool-using decisions.
Before submitting a job, an ML engineer needs to establish that the model and region are supported, the agent can participate in SageMaker’s rollout loop, the prompts and rewards represent the intended search task, and a separate evaluation set can expose regressions. This guide is based on AWS documentation; BIG CHANGE did not run an AWS job or call its APIs.
