AWS has published new technical guidance on designing custom reward functions for multi-turn reinforcement learning using Amazon Nova Forge, the company's platform for customizing and fine-tuning its Nova family of models. The post, published on the AWS Machine Learning Blog, walks through how developers can define reward signals that evaluate an AI agent's behavior across an entire multi-step conversation or task, rather than judging each response in isolation.
The distinction matters because most reinforcement learning setups historically optimized for single-turn quality — does this one reply look correct, helpful, or on-brand. Multi-turn reward functions instead score an agent based on outcomes that only become visible over a sequence of exchanges: did the conversation converge on a resolution, did the agent stay consistent with earlier commitments, did it avoid unnecessary repetition or escalation. AWS's guidance frames this as necessary for training agents that operate in realistic, extended interactions rather than scripted one-shot exchanges.
According to the source material, Nova Forge now supports defining these reward functions as custom code that developers write and plug into the training loop, giving them direct control over what the model is optimized to do across a session. This is a meaningful shift from relying purely on generic preference models or human feedback labels, both of which can be expensive to collect at the scale needed for reinforcement learning and don't always capture domain-specific success criteria.
The practical implication AWS highlights is that teams building specialized agents — for customer service, technical support, or task automation — can now encode their own definition of success directly into the training signal. A support agent, for instance, could be rewarded for resolving a ticket in fewer turns while adhering to escalation policy, rather than simply for producing responses that sound plausible.
This development is currently limited to teams using Nova Forge specifically for model customization on AWS infrastructure; it is not a general-purpose feature available across all reinforcement learning tooling, and AWS's post does not include independent benchmark comparisons against other reward-modeling approaches, so any performance claims beyond what AWS describes should be treated as unconfirmed. The company has not published pricing specifics for the reward-function customization workflow beyond what's covered in the existing Nova Forge documentation.
For companies not building models directly, the underlying concept — evaluating agents on full-task outcomes rather than single responses — is the more transferable takeaway, and one that applies whether the agent in question was built on Nova, a competing foundation model, or an internal automation stack.