Multi-Turn Reinforcement Learning on Amazon SageMaker AI: A hands-on guide

Most fine-tuning teaches a model to produce a better answer to a single prompt. Agentic tasks are different. An agent that files an expense report or triages a support ticket takes a sequence of actions, and each step depends on what came before. There’s rarely one correct sequence to imitate, and the only reliable feedback is whether the whole sequence succeeded. Supervised fine-tuning has no labels for that, and single-turn reinforcement learning cannot credit a decision made three steps before the outcome.

Multi-Turn Reinforcement Learning (MTRL) on Amazon SageMaker AI trains models on exactly that signal. SageMaker AI sends prompts to an agent you host on Amazon Bedrock AgentCore, or on your own infrastructure, such as Amazon Elastic Kubernetes Service (Amazon EKS), Amazon Elastic Compute Cloud (Amazon EC2), or AWS Fargate, through an AWS Lambda forwarder . The example here uses AgentCore. The agent calls the policy model being trained, acts in your environment over as many turns as the task needs, and reports one reward when the episode ends. You don’t specify the right trajectory, only how to score the result.

This post uses Wordle , a game in which a player has six tries to guess a hidden five-letter word, as the example. After each guess, every letter turns green if it is in the right position, yellow if it’s in the word but in a different position, and gray if it isn’t in the word. The game suits MTRL because the rules are few, each guess depends on the feedback from the last, and the outcome is scored objectively. Wordle is a teaching example, and frontier models already play it well. In practice, you would use MTRL to make a small, low-cost model good at a multi-step task specific to your business, such as an internal workflow that public models were never trained on. The same pipeline applies to that setting.

For every rollout, the MTRL service invokes the AgentCore runtime, a capability of Amazon Bedrock AgentCore, with one training row, which holds one secret word. A Strands agent in the runtime calls the policy model through the SageMaker Job Runtime’s OpenAI-compatible endpoint. The model plays up to six guesses through one tool, guess_word , and after each guess the environment returns per-letter feedback and a summary of what is known. When the game ends, the agent returns one reward. Group Relative Policy Optimization (GRPO) uses the spread of rewards across several rollouts of the same word to update a Low-Rank Adaptation ( LoRA ) adapter on the policy model.

Figure 2: The Wordle agent runtime in the Amazon Bedrock AgentCore console with status Ready

The train dimension dominates and scales with trajectory length, so shorter games and less verbose reasoning cut the bill. The AgentCore runtime bills separately for the vCPU and memory the agent uses. For this run, that was about $79, from the runtime’s Amazon CloudWatch usage metrics at us-east-1 list prices. DescribeJob reports the token counts under BillableTokenUsage . See Amazon SageMaker AI pricing for current rates.

The runtime reads two environment variables from agentcore.json . REASONING_EFFORT=low sets reasoning_effort on every request to the policy model. In our evaluation jobs, low effort used 63 to 83 percent fewer sample tokens per game than medium. REASONING_PROMPT_HINT=true also adds “Reasoning: low” to the system prompt, which is how gpt-oss reads the setting in its Harmony chat format. Neither variable gives the model any hint about the secret word.

In our run, MTRL took gpt-oss-20b from a 61 to an 84 percent solve rate on held-out words at its default reasoning effort, and from 15 to 69 percent at the low effort it trained at, for about $339 in training tokens. In this post, we walk through the pipeline end-to-end: deploying the agent and reward to AgentCore, preparing the dataset, training, monitoring in MLflow, and evaluating at serving settings. We also share the reward-design mistakes and training failures we hit. Your datasets, agent, and trained model stay in your AWS account and are not used to train the base models . All code is in the accompanying aws-samples repository .

The system has two halves. SageMaker AI owns the training job and the policy model. AgentCore hosts the agent, the Wordle environment, and the reward function. The following diagram shows how they connect.

Figure 1: MTRL on SageMaker AI, where the training job invokes the AgentCore-hosted agent once per rollout to sample the policy model, play the game, and report a reward