Amazon Nova Forge Introduces: A Guide to Designing Custom Reward Functions for Multi-Turn Reinforcement Learning

Summary:AWS has released an in-depth guide on Amazon Nova Forge, demonstrating how to design custom reward functions for multi-turn reinforcement learning (RL) tasks. Nova Forge leverages its Bring Your Own Orchestration (BYOO) capability, enabling developers to execute reward logic within their own environments, while also offering a serverless option for simplified deployment. The guide emphasizes key principles in reward design, such as constructing multi-component rewards to balance behavior and out

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Amazon Nova Forge: A Guide to Designing Custom Reward Functions for Multi-Turn Reinforcement Learning

In multi-turn reinforcement learning (RL), the custom reward function dictates what the model actually learns. Designing a reward function that remains effective across multi-turn tasks is one of the most challenging aspects of customizing Amazon Nova models. Amazon Nova Forge, through its Bring Your Own Orchestration (BYOO) capability, allows developers to execute reward logic within their own environments, while also offering a serverless option for simplified deployment.

Key Content Overview

1. Principles of Reward Function Design

   • Multi-Component Rewards: Combine outcome rewards, behavioral rewards, and penalties to balance the model's learning objectives. For example, in a programming task, the reward function might include task correctness, behavioral signals, and penalties (such as guessing or repetition).
   • Avoiding Reward Collapse: Ensure each component has sufficient within-group variance to prevent the reward signal from degenerating. For instance, avoid using overly sparse rewards or setting unreasonable efficiency terms.
   • Safely Executing Model-Generated Code: Run code in an isolated environment and implement measures such as resource limits and random sentinel validation to prevent the model from generating malicious or unreliable code.

2. Key Implementation Details

   • Reward Function Implementation: Use AWS Lambda or a custom container environment (such as Amazon ECS) to implement the reward logic, and report each component's score through metrics_list.
   • Training Configuration: Use an Amazon SageMaker HyperPod cluster for training and manage multi-turn interactions and conversation states.

3. Common Issues and Solutions

   • Reward Collapse: If a component in the reward function always returns the same value, its within-group variance is zero, leading to learning stagnation. The solution is to track each component's within-group standard deviation and ensure the target behavior is rewardable and failure modes are explicitly penalized.
   • Component Failure: For example, a correctness scorer that always returns zero due to a setup error, causing the model to fail to learn. The solution is to verify the actual execution of the component and ensure its output has sufficient variance.

4. Developer Recommendations

   • Monitoring and Debugging: Use a per-component advantage-variance panel to automatically detect dead channels and sort transcripts by component to quickly identify issues.
   • Designing Robust Reward Functions: Avoid overly complex reward logic and ensure the reward signal effectively guides the model toward the target behavior.

Technical Highlights

• Multi-Component Reward Design: By combining task correctness, behavioral signals, and penalties, the reward function can more comprehensively guide the model.
• BYOO Capability: Allows developers to execute reward logic within their own environments, providing greater flexibility and control.
• Safe Execution of Model-Generated Code: Through resource limits, random sentinel validation, and other measures, the platform ensures the security of model-generated code.

Industry Impact and Developer Recommendations

The release of Amazon Nova Forge provides powerful tool support for multi-turn reinforcement learning tasks, particularly in scenarios requiring complex reward design. Developers can leverage this platform to design more effective reward functions, thereby enhancing the performance of models in complex tasks. At the same time, developers should pay attention to the design principles and common issues of reward functions and use tools for effective monitoring and debugging to ensure the effectiveness and stability of the reward signal.

Conclusion

Amazon Nova Forge offers an innovative solution for multi-turn reinforcement learning tasks. By designing and executing custom reward functions, developers can more effectively guide the model toward target behaviors. This article provides a detailed introduction to the design principles, implementation details, and solutions to common issues of reward functions, offering valuable reference for developers.

Source

This content is based on the original article from the AWS Machine Learning Blog titled "Custom reward functions for multi-turn reinforcement learning with Amazon Nova Forge."

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Source: AWS Machine Learning Blog (2026-08-14)

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Tags: #Amazon #Reinforcement Learning #Multi-Turn RL #Reward Functions #Amazon Nova