AWS published a guide on designing custom reward functions for multi-turn reinforcement learning using Amazon Nova Forge. The post explains that in multi-turn RL, the custom reward function determines what a model actually learns. It walks through building a composite multi-turn reward, executing model-generated code safely inside the reward function, and instrumenting each component to detect pitfalls that can quietly cause a reward to collapse.
Why it matters
Reward design is central to reinforcement learning outcomes. By addressing composite rewards, safe code execution, and component-level instrumentation, the guidance targets common failure points that affect training reliability.
Who should care
Practitioners working with Amazon Nova Forge and teams applying multi-turn reinforcement learning who need to build and debug custom reward functions.