How to build effective reward functions with AWS Lambda for Amazon Nova model customization
Amazon AWS AI Blog / 4/14/2026
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Key Points
- The post explains how to use AWS Lambda to implement scalable, cost-effective reward functions for Amazon Nova model customization.
- It compares two approaches—RLVR for objectively verifiable tasks and RLAIF for subjective evaluation—so teams can select the right reward strategy.
- It provides guidance on designing multi-dimensional reward systems to reduce the risk of reward hacking.
- It covers practical steps for optimizing Lambda functions to support training at scale and for monitoring reward distributions using Amazon CloudWatch.
- The article includes working code examples and deployment instructions to help readers quickly prototype and iterate.
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 you prevent reward hacking, optimize Lambda functions for training scale, and monitor reward distributions with Amazon CloudWatch. Working code examples and deployment guidance are included to help you start experimenting.
