FUNDING · COMPANIES · #2506
Hands-on guide: Multi-Turn Reinforcement Learning (MTRL) on Amazon SageMaker AI with AgentCore
Amazon published a hands-on guide demonstrating Multi-Turn Reinforcement Learning (MTRL) on SageMaker AI that uses an agent hosted on Amazon Bedrock AgentCore (or on user infrastructure) to sample policy models and return episode-level rewards. The post walks an end-to-end pipeline (deployment, dataset prep, LoRA training with GRPO, MLflow monitoring, evaluation), uses Wordle as a toy example, reports improving GPT-OSS-20B solve rate from 61% to 84% (and from 15% to 69% at a lower reasoning effort) for about $339 in training tokens, and provides code in an aws-samples repo.
KEY POINTS
- Amazon published a hands-on guide demonstrating Multi-Turn Reinforcement Learning (MTRL) on SageMaker AI that uses an agent hosted on Amazon Bedrock AgentCore (or on user infrastructure) to sample policy models and return episode-level rewards.
- The post walks an end-to-end pipeline (deployment, dataset prep, LoRA training with GRPO, MLflow monitoring, evaluation), uses Wordle as a toy example, reports improving GPT-OSS-20B solve rate from 61% to 84% (and from 15% to 69% at a lower reasoning effort) for about $339 in training tokens, and provides code in an aws-samples repo.
- Enables serverless, outcome-driven fine-tuning of policy models for multi-step agent tasks (no per-step labels) on SageMaker AI and provides concrete cost/performance data and reproducible code for practitioners.
WHY IT MATTERS
Enables serverless, outcome-driven fine-tuning of policy models for multi-step agent tasks (no per-step labels) on SageMaker AI and provides concrete cost/performance data and reproducible code for practitioners.