Fine-tune Amazon Nova models with reinforcement fine-tuning

Provide Training Dataset – Upload prompts in required format (e.g., JSONL) as the reinforcement fine-tuning training dataset. For more information, see Prepare data for Amazon Nova models.

Configure Reward Function (grader) – Define a grader to score model responses based on correctness, structure, tone, or other objectives. The reward function can be executed using Lambda to compute objective scores. You can also choose a model as a judge (via console) and grade responses based on criteria and principles you configure (the console converts these into Lambda functions automatically). For more information, see Setting up reward functions for Amazon Nova models.

Submit reinforcement fine-tuning Job – Launch the reinforcement fine-tuning job by specifying base model, dataset, reward function, and other optional settings such as hyperparameters. For more information, see Create and manage fine-tuning jobs for Amazon Nova models.

Monitor Training – Track job status, reward metrics, and training progress until completion. For more information, see Monitor your RFT training job.

Use Fine-Tuned Model – After job completion, deploy the resulting RFT model with one click for on-demand inference. You can also use Provisioned Throughput for mission-critical workloads that require consistent performance. See Set up inference for a custom model. Use Test in Playground to evaluate and compare responses with the base model.