What happened
Amazon published guidance on designing custom reward functions for multi-turn reinforcement learning with Amazon Nova Forge, its model customization platform. The post covers how to structure composite rewards combining outcome signals (task success), behavioral signals (intermediate steps like asking before acting), and penalties (failure modes), using a worked example where Nova Lite 2.0 learns to ask clarifying questions before writing code on 500 programming tasks.
Why it matters
Reward functions silently shape what models learn—a subtle mistake teaches the wrong behavior while training curves look healthy. The post demonstrates how to instrument reward components so teams can catch when high-weighted parts contribute no learning signal, a real failure mode the authors encountered. For businesses customizing Nova models for agentic tasks (code execution, tool calling, multi-step workflows), this is practical guidance on avoiding costly training mistakes.
What to watch
Amazon Nova Forge now offers a serverless multi-turn RL option (generally available) for teams avoiding custom infrastructure, alongside the Bring Your Own Orchestration (BYOO) path shown here. The worked example uses Amazon SageMaker HyperPod, LoRA fine-tuning, and GRPO (Group Relative Policy Optimization) on 500 unique programming tasks; code is available in the aws-samples/sample-nova-multi-turn-rl-infra repository.
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Reinforcement fine-tuning (RFT) with custom rewards is central to Amazon Nova customization because it teaches models through iterative feedback rather than requiring pre-annotated reasoning paths (as supervised fine-tuning does). Multi-turn RFT extends this to agents acting over sequences of steps—calling tools, executing code, recovering from errors—and optimizes cumulative reward across the entire trajectory. The core challenge, which this post addresses, is that a subtly wrong reward can quietly teach the wrong behavior while all training metrics appear healthy.
The post highlights a critical insight about how GRPO (the optimization algorithm Nova Forge uses) operates: a reward component contributes to learning only through variation it creates within a batch. If a component returns the same score for every completion, it produces zero gradient, contributing nothing to training—a failure mode the authors encountered with a highest-weighted component that should have driven learning but silently did not. This means reward design requires not just defining outcome metrics, but explicitly structuring components to maintain variation and penalizing failure modes so the optimizer sees a clear gradient between good and bad strategies.
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