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

This article explains how to build effective reward functions using AWS Lambda for customizing Amazon Nova models through Reinforcement Fine-Tuning (RFT).

• Lambda provides scalable, serverless infrastructure for evaluating model responses during training
• RLVR (Reinforcement Learning via Verifiable Rewards) uses deterministic code for objective tasks like math and code generation
• RLAIF (Reinforcement Learning via AI Feedback) uses AI judges for subjective tasks like tone and helpfulness evaluation
• Multi-dimensional reward systems prevent reward hacking by evaluating multiple quality criteria simultaneously
• Optimization techniques include batch sizing tuning, concurrency management, cold start mitigation, and error handling
• CloudWatch monitoring provides visibility into reward distributions and training progress
• Lambda automatically scales from 10 to 400+ concurrent evaluations without infrastructure management

Lambda-based reward functions make Nova customization accessible and cost-effective by combining serverless scalability with flexible evaluation logic for both objective and subjective quality assessment.