Amazon Web Services has published guidance on building reward functions using AWS Lambda for customizing Amazon Nova models, according to a post on the Amazon AWS AI blog. The approach enables scalable and cost-effective reward functions for model customization.
According to the post, developers can choose between two reinforcement learning methods depending on their use case. Reinforcement Learning via Verifiable Rewards (RLVR) is designed for objectively verifiable tasks, while Reinforcement Learning via AI Feedback (RLAIF) is suited for subjective evaluations. The Lambda-based implementation allows organizations to customize Amazon Nova models without requiring extensive infrastructure.
The post demonstrates practical implementation approaches for both RLVR and RLAIF methods within the AWS Lambda framework. By leveraging Lambda’s serverless architecture, developers can create reward functions that scale based on demand while managing costs effectively. This methodology provides organizations with flexible options for fine-tuning Amazon Nova models according to specific requirements, whether those requirements involve objective metrics or more nuanced, subjective assessments.