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

This article explains reinforcement fine-tuning (RFT) for Amazon Nova models, a technique that teaches AI through evaluation rather than imitation, requiring only prompts and quality criteria instead of massive labeled datasets.

• RFT learns by evaluating outcomes through test cases and reward functions instead of imitating labeled examples
• Supports code generation and math reasoning by verifying outputs automatically without step-by-step demonstrations
• Available across four tiers: Amazon Bedrock (fully managed), SageMaker Training Jobs (flexible control), SageMaker HyperPod (enterprise-scale), Nova Forge (multi-turn agentic workflows)
• Uses two reward approaches: RLVR (rule-based Lambda functions) for objective tasks, RLAIF (AI judges) for subjective evaluation
• Ideal for code generation, customer service, content moderation, financial analysis where outcomes are verifiable
• Requires model to produce at least one correct solution among 4-8 attempts; use SFT first if consistently failing
• Supports LoRA (parameter-efficient, lower cost) and full-rank training with different resource tradeoffs
• Works with reasoning models that show intermediate thinking steps for complex analytical tasks
• Reduces token usage and operational complexity compared to supervised fine-tuning

RFT enables efficient model customization for tasks with verifiable outcomes, offering a scalable alternative to traditional supervised fine-tuning across multiple implementation tiers.