Reinforcement fine-tuning on Amazon Bedrock: Best practices

You can use reinforcement Fine-Tuning (RFT) in Amazon Bedrock to customize Amazon Nova and supported open source models by defining what “good” looks like—no large labeled datasets required. By learning from reward signals rather than static examples, RFT delivers up to 66% accuracy gains over base models at reduced customization cost and complexity. This post covers best practices for RFT on Amazon Bedrock, from dataset design, reward function strategy, and hyperparameter tuning for use cases like code generation, structured extraction, and content moderation.

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.

RFT use-cases: Where can RFT shine?

Reinforcement ...