Amazon Nova offers multiple customization approaches, with Reinforcement fine-tuning (RFT) standing out for its ability to teach models desired behaviors through iterative feedback. Unlike Supervised fine-tuning (SFT) that requires thousands of labeled examples with annotated reasoning paths, RFT learns from evaluation signals on final outputs. At the heart of RFT lies the reward function—a scoring mechanism that guides the model toward better responses.

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.

You have multiple pathways to customize foundation models, each suited to different scenarios. SFT excels when you have clear input-output examples and want to teach specific response patterns—it’s particularly effective for tasks like classification, named entity recognition, or adapting models to domain-specific terminology and formatting conventions. SFT works well when the desired behavior can be demonstrated through examples, making it ideal for teaching consistent style, structure, or factual knowledge transfer.However, some customization challenges require a different approach. When applications need models to balance multiple quality dimensions simultaneously—like customer service responses that must be accurate, empathetic, concise, and brand-aligned simultaneously —or when creating thousands of annotated reasoning paths proves impractical, reinforcement-based methods offer a better alternative. RFT addresses these scenarios by learning from evaluation signals rather than requiring exhaustive labeled demonstrations of correct reasoning processes.

AWS Lambda-based reward functions simplifies this through feedback-based learning. Instead of showing the model thousands of effective examples, you provide prompts and define evaluation logic that scores responses—then the model learns to improve through iterative feedback. This approach requires fewer labelled examples while giving you precise control over desired behaviors. Multi-dimensional scoring captures nuanced quality criteria that prevent models from exploiting shortcuts, while Lambda’s serverless architecture handles variable training workloads without infrastructure management. The result is Nova customization that’s accessible to developers without deep machine learning expertise, yet flexible enough for sophisticated production use cases.

The RFT architecture uses AWS Lambda as a serverless reward evaluator that integrates with Amazon Nova training pipeline, creating an feedback loop that guides model learning. The process begins when your training job generates candidate responses from the Nova model for each training prompt. These responses flow to your Lambda function, which evaluates their quality across dimensions like correctness, safety, formatting, and conciseness. The function then returns scalar numerical scores—typically in the -1 to 1 range as a best practice. Higher scores guide the model to reinforce the behaviors that produced them, while lower scores guide it away from patterns that led to poor responses. This cycle repeats thousands of times throughout training, progressively shaping the model toward responses that consistently earn higher rewards.