Reinforcement fine-tuning for Amazon Nova: Teaching AI through feedback

Foundation models deliver impressive out-of-the-box performance for general tasks, but many organizations need models to consume their business knowledge. Model customization helps you bridge the gap between general-purpose AI and your specific business needs when building applications that require domain-specific expertise, enforcing communication styles, optimizing for specialized tasks like code generation, financial reasoning, or ensuring compliance with industry regulations. The challenge lies in how to customize effectively. Traditional supervised fine-tuning delivers results, but only if you have thousands of carefully labeled examples showing not just the correct final answer, but also the complete reasoning path to reach it. For many real-world applications, especially those tasks where multiple valid solution paths exist, creating these detailed step-by-step demonstrations can sometimes be expensive, time-consuming.

In this post, we explore reinforcement fine-tuning (RFT) for Amazon Nova models, which can be a powerful customization technique that learns through evaluation rather than imitation. We’ll cover ...