Custom reward functions for multi-turn reinforcement learning with Amazon Nova Forge

Amazon Nova Forge enables multi-turn reinforcement learning with custom reward functions, allowing for more precise control over model learning. The platform's Bring Your Own Orchestration (BYOO) capability and serverless option provide flexibility in deploying and managing custom reward logic. By designing a well-crafted reward function, engineers can teach models to learn specific behaviors through iterative feedback, optimizing cumulative reward across entire trajectories. This approach has been shown to improve out-of-distribution (OOD) generalization, with reinforcement fine-tuning (RFT) outperforming supervised fine-tuning (SFT) in certain scenarios. For engineers building AI systems, this means that careful consideration of reward function design is crucial for effective model training.

⚡ Key Takeaways

• Amazon Nova Forge provides a Bring Your Own Orchestration (BYOO) capability for running custom reward logic in multi-turn reinforcement learning.
• Reinforcement fine-tuning (RFT) can learn from evaluation signals on the model's own outputs, optimizing cumulative reward across entire trajectories.
• Group Relative Policy Optimization (GRPO) can learn from composite multi-turn rewards, requiring careful design of the reward function.
• Instrumenting each component of the reward function is essential to trust what training is learning and catch potential pitfalls.
• The Nova Customization SDK and multi-turn RFT APIs are available through an Amazon Nova Forge subscription.

For engineers shipping production AI models, the ability to customize reward functions and optimize model learning through multi-turn reinforcement learning can significantly improve model performance and generalization. By leveraging Amazon Nova Forge and carefully designing reward functions, engineers can create more effective and robust AI systems.

✅ Practical Steps

1. Set up an Amazon Nova Forge subscription and access the Nova Customization SDK and multi-turn RFT APIs.
2. Configure the multi-turn RFT infrastructure, including an Amazon SageMaker HyperPod cluster and an Amazon S3 bucket for rollout data and checkpoints.
3. Implement a custom reward environment using the example code from the aws-samples/sample-nova-multi-turn-rl-infra repository.
4. Instrument each component of the reward function to ensure trust in what training is learning.

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