In this post, we explore where RFT is most effective, using the GSM8K mathematical reasoning dataset as a concrete example. We then walk through best practices for dataset preparation and reward function design, show how to monitor training progress using Amazon Bedrock metrics, and conclude with practical hyperparameter tuning guidelines informed by experiments across multiple models and use cases.

Reinforcement Fine-Tuning (RFT) is a model customization technique that improves foundation model (FM) behavior using reward signals. Compared to supervised fine-tuning (SFT), it doesn’t directly train on correct responses (labeled I/O pairs). Instead, RFT uses a dataset of inputs and a reward function. The reward function can be rule-based or another trained grader model, or large language model (LLM) as a judge. During training, the model generates candidate responses and the reward function scores each response. Based on the reward, the model weights are updated to increase the probability of generating responses that receive a high reward. This iterative cycle of sample responses, score responses, and update weights steers the model to learn which behaviors lead to better outcomes. RFT is particularly valuable when the desired behavior can be evaluated, but difficult to demonstrate—whether because labeled data is impractical to curate or because static examples alone can’t capture the reasoning a task demands. It excels in two primary areas:

Tasks in the first category are code generation that must pass tests, math reasoning with verifiable answers, structured data extraction that must match strict schemas, or API/tool calls that must parse and execute correctly. Because success criteria can be translated directly into reward signals, the model can discover stronger strategies than what a small set of labeled examples could teach. This pattern is known as Reinforcement Learning with Verifiable Rewards (RLVR).

In addition, RFT suits subjective tasks such as content moderation, chatbots, creative writing, or summarization that lack easily quantifiable correctness. A judge model, guided by a detailed evaluation rubric, can serve as the reward function. It scores outputs against criteria that would be impractical to encode as static training pairs. This approach is known as Reinforcement Learning with AI Feedback (RLAIF).

For RFT in Amazon Bedrock, you can implement both rule-based and model-based approaches as a custom AWS Lambda function, which is the reward function that Amazon Bedrock calls during the training loop.