Core Technical Signal
The AWS Machine Learning Blog announces the integration of AWS Lambda with Amazon Nova for model customization, specifically for building effective reward functions using Reinforcement fine-tuning (RFT). The RFT architecture uses AWS Lambda as a serverless reward evaluator, which integrates with the Amazon Nova training pipeline to guide model learning.
Where to Find the Primary Source
The primary source is the AWS Machine Learning Blog post, which provides detailed information on how to build effective reward functions with AWS Lambda for Amazon Nova model customization. The post includes working code examples and deployment guidance for developers to start experimenting.
The Structural Shift Frame
Amazon Nova model customization is shifting from Supervised fine-tuning (SFT) to Reinforcement fine-tuning (RFT) with the integration of AWS Lambda, enabling developers to define quality criteria and evaluate responses across multiple dimensions.
Early Warning — What To Do First
Developers can start by exploring the AWS Lambda-based reward functions for Amazon Nova customization, using tools such as Amazon SageMaker AI Studio to maintain consistent quality measurement across multiple training runs. They can also use Amazon CloudWatch to monitor reward distributions and training progress, and trigger alerts when issues arise. Additionally, developers can choose between Reinforcement Learning via Verifiable Rewards (RLVR) and Reinforcement Learning via AI Feedback (RLAIF) to fine-tune their models, depending on the specific use case and requirements.