AWS: How to build reward functions with Lambda for fine-tuning Amazon Nova models

AWS has published a comprehensive technical guide showing how to use AWS Lambda for creating reward functions when fine-tuning Amazon Nova models. The guide is a practical resource for engineers looking to customize models for specific business needs.

Two approaches to rewarding

The guide covers two key approaches:

RLVR (Reinforcement Learning with Verifiable Rewards) uses objective answer verification — ideal for tasks where there is a clearly correct or incorrect answer, such as math problems or code generation.

RLAIF (Reinforcement Learning from AI Feedback) uses another AI model to evaluate response quality — more suitable for subjective tasks like creative writing or customer support.

Practical implementation

The guide describes in detail how to design multi-dimensional reward systems that can simultaneously optimize for multiple objectives — for example, accuracy, helpfulness, and safety of responses. AWS Lambda enables scalable execution of these functions without managing infrastructure.

Who this is useful for

The guide is aimed at ML engineers and data scientists using Amazon Bedrock for model customization. It includes practical tips for optimizing Lambda performance and monitoring results through CloudWatch, making the process transparent and measurable.