How to build effective reward functions with AWS Lambda for Amazon Nova model customization

Building effective reward functions can help you customize Amazon Nova models to your specific needs, with AWS Lambda providing the scalable, cost-effective foundation. Lambda’s serverless architecture lets you focus on defining quality criteria while it handles the computational infrastructure.

Amazon Nova offers multiple customization approaches, with Reinforcement fine-tuning (RFT) standing out for its ability to teach models desired behaviors through iterative feedback. Unlike Supervised fine-tuning (SFT) that requires thousands of labeled examples with annotated reasoning paths, RFT learns from evaluation signals on final outputs. At the heart of RFT lies the reward function—a scoring mechanism that guides the model toward better responses.

This post demonstrates how Lambda enables scalable, cost-effective reward functions for Amazon Nova customization. You’ll learn to choose between Reinforcement Learning via Verifiable Rewards (RLVR) for objectively verifiable tasks and Reinforcement Learning via AI Feedback (RLAIF) for subjective evaluation, design multi-dimensional reward systems that help ...