Multi-Turn Reinforcement Learning on Amazon SageMaker AI: A practical guide
In brief
- Amazon SageMaker AI introduces multi-turn reinforcement learning (MTRL) to train agents on complex sequential tasks.
- The Wordle example shows gpt-oss-20b’s success rate improving from 61 % to 84 % on unseen words, for a cost of $339.
- The pipeline covers deployment, data preparation, training, and evaluation, with specific IAM roles and environment variables.
Amazon SageMaker AI introduces a multi-turn reinforcement learning (MTRL) method to train models on complex sequential tasks. According to the announcement, this approach teaches agents to perform chained actions, such as filling out an expense report, where each step depends on the previous ones. Unlike supervised learning or classic reinforcement learning, MTRL evaluates the overall success of the sequence rather than isolated steps.
The hands-on guide uses Wordle as a teaching example to illustrate the full pipeline. The agent, hosted on Amazon Bedrock AgentCore, interacts with the Wordle environment through dedicated tools and receives a single feedback at the end of each game. The base model, gpt-oss-20b, improves its success rate from 61 % to 84 % on unseen words, at a cost of about $339 for 105,486 games in 12 hours 40 minutes. The system relies on specific IAM roles and environment variables to control the model’s reasoning effort.
This framework applies to internal workflows distinct from public tasks. Costs depend on token count and sequence length, with separate pricing for the agent infrastructure. Code and data remain within the user’s AWS account and do not contribute to base models.
This summary gives you the essentials. The full article, its figures and quotes are on Artificial Intelligence.
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