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

Amazon has released a detailed technical guide demonstrating how developers can use AWS Lambda to build scalable, cost-effective reward functions for customizing Amazon Nova foundation models through Reinforcement Fine-Tuning (RFT). Unlike traditional Supervised Fine-Tuning (SFT) that requires thousands of meticulously labeled examples with annotated reasoning paths, RFT learns from evaluation signals on final outputs, reducing data requirements by approximately 90%. The Lambda-based approach provides a serverless architecture that automatically scales to handle variable training workloads without infrastructure management, allowing developers to focus on defining quality criteria through code-based reward logic.

The guide outlines two primary approaches: Reinforcement Learning via Verifiable Rewards (RLVR) for objectively measurable tasks and Reinforcement Learning via AI Feedback (RLAIF) for subjective evaluation scenarios. Developers can implement multi-dimensional scoring systems that evaluate responses across criteria like accuracy, safety, formatting, and conciseness, typically using a -1 to 1 scoring range. This prevents "reward hacking" where models exploit shortcuts to maximize scores without genuinely improving quality. The architecture integrates Lambda with Amazon Bedrock's managed RFT pipeline, creating a feedback loop where the model generates responses, Lambda evaluates them, and the model learns to produce higher-scoring outputs over thousands of training iterations.

Working code examples and deployment guidance enable immediate experimentation, making sophisticated model customization accessible to developers without deep machine learning expertise. This approach is particularly valuable for applications requiring balanced quality dimensions—like customer service responses that must simultaneously demonstrate accuracy, empathy, conciseness, and brand alignment—where traditional SFT struggles with the complexity of demonstrating all desired behaviors through examples alone.