// AI news · 2026-10-24 · AWS Machine Learning Blog
Multi-Turn RL on SageMaker AI with AgentCore
Amazon SageMaker AI introduces multi-turn reinforcement learning (MTRL) on AgentCore, enabling policy models to learn sequential decision-making through agent-environment interactions.
Published 2026-10-24 01:48 (Beijing time)Source AWS Machine Learning BlogAuto-summarised, fact-checked against source
01Summary
Using gpt-oss-20b, solve rates improved from 61% to 84% in default reasoning mode and from 15% to 69% at low effort. Training costs approximately $339 across 105,486 rollouts, with train tokens dominating due to trajectory length. The system uses LoRA adapters and runs on a serverless architecture with full control over base models and training cost by token type.
Summarised from the original article by an open-source model, then checked automatically: every number and English name must appear in the source. The original article is authoritative.
02Key points
- MTRL enables policy models to learn sequential decisions via agent-environment interaction.
- gpt-oss-20b solve rate increased from 61% to 84% in default mode and 15% to 69% at low effort.
- Training cost is ~$339, with train tokens dominating due to trajectory length.
- Serverless architecture supports LoRA adapters and runs on user-selected base models.
03Source
Multi-Turn Reinforcement Learning on Amazon SageMaker AI: A hands-on guide
AWS Machine Learning Blog · published 2026-10-24 01:48 Beijing time
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