Amazon Web Services has published guidance on designing custom reward systems for multi-turn reinforcement learning workflows. In these complex setups, the specific reward architecture serves as the core mechanism steering model outcomes. Specifically, the custom reward function dictates what the artificial intelligence system actually learns over extended multi-turn interactions. Developers working with Amazon Nova Forge must structure these signals carefully to guide model optimization effectively.
The guidance demonstrates practical approaches for structuring composite multi-turn rewards while managing runtime security. This architecture includes methods to execute model-generated code safely directly inside the evaluation pipeline. Furthermore, engineering teams are advised to instrument each component of the reward calculation. Thorough instrumentation helps teams detect operational vulnerabilities and avoid the subtle pitfalls that quietly collapse a reward mechanism.