Amazon recently introduced a method to customize its Nova language models using AWS Lambda for reward functions. This approach allows developers to scale reward systems efficiently while keeping costs low. Two main techniques are available: Reinforcement Learning via Verifiable Rewards (RLVR) for tasks that can be objectively measured and Reinforcement Learning via AI Feedback (RLAIF) for evaluations that depend on subjective judgment.
The system supports multi-dimensional reward functions to reduce the risk of reward hacking, where models exploit loopholes in the evaluation criteria. AWS Lambda processes these functions in real time, enabling faster adjustments and iterations. Developers can also optimize Lambda configurations to handle larger training workloads without proportional cost increases.
Monitoring is handled through Amazon CloudWatch, which tracks reward distributions and flags anomalies. This ensures reward signals remain balanced and effective during training. The setup is designed for flexibility, allowing teams to adapt reward structures as model behavior evolves.
AWS documentation confirms the method has been tested in production environments. Teams using this approach report improved model alignment with intended outcomes, particularly in scenarios requiring nuanced feedback.
Source: aws.amazon.com