AWS shows multi-turn RL fine-tuning for search agents on SageMaker AI
AWS published a tutorial on using Amazon SageMaker AI's multi-turn reinforcement learning (MTRL) to fine-tune a Qwen3.6-27B model into a search agent. The agent uses BM25 and vector search tools across multiple interaction turns to retrieve information. Training used nDCG@10 as a trajectory-level reward, with a -1 penalty for hitting turn or token limits.
Key points
- Fine-tuned Qwen3.6-27B with SageMaker AI MTRL for multi-turn search agent
- nDCG@10 improved up to 23.7% on BrowseComp-Plus, failure rate fell to 0.68%
- Serverless training with default PPO/CISPO algorithms, per-token pricing
The fine-tuned model improved nDCG@10 on three of four benchmarks: +23.7% on BrowseComp-Plus, +18.4% on WixQA, and +6% on Wands, with a slight regression on FreshStack. Failure rates dropped sharply, from 22.89% to 0.68% on BrowseComp-Plus. Training ran with maxepochs=1, globalbatchsize=128, and rolloutmaxconcurrency=32 on serverless infrastructure in the US West (Oregon) region.
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Fine-tune a search agent with multi-turn RL on Amazon SageMaker AI
AWS Machine Learning Blog · 2 October 2026
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