In multi-turn reinforcement learning (RL), your custom reward function decides what the model actually learns. A subtly wrong reward can quietly teach the wrong thing while every training curve looks healthy. Designing a reward that holds up over multi-turn, agentic tasks is one of the hardest parts of customizing Amazon Nova models. For multi-turn training, Amazon Nova Forge runs your reward logic in your own environment through its Bring Your Own Orchestration (BYOO) capability. You can focus on defining what a good outcome looks like while Nova Forge coordinates rollouts, message passing, and conversation state across turns. Nova Forge also offers a serverless multi-turn RL option, now generally available, for teams that prefer not to manage that environment. This post uses the BYOO path.

Amazon Nova offers multiple customization approaches, with reinforcement fine-tuning (RFT) standing out because it can teach models the behaviors you want through iterative feedback. RFT takes a different approach from supervised fine-tuning (SFT). Rather than requiring curated examples with annotated reasoning paths, it learns from evaluation signals on the model’s own outputs. Multi-turn RFT extends this to agents that act over a sequence of steps, such as calling tools, executing code, or recovering from a mistake. It optimizes cumulative reward across the whole trajectory rather than grading a single response. At the heart of RFT lies the reward function: the scoring mechanism that guides the model, and the part you design.