AWS explains how to fine-tune Amazon Nova with an LLM judge for complex enterprise tasks

AWS has laid out in detail how to apply reinforcement fine-tuning with an LLM-as-a-judge approach to Amazon Nova models. Instead of manual labeling or a set of rigid rules, a separate language model evaluates the quality of each answer, and its verdict becomes the reward signal for training.

Why you need a judge

According to AWS, standard RFT can be built either on verifiable rules, such as an exact string match, or on a setup where another LLM scores the answer against several criteria at once. The second option is for cases where quality cannot be reduced to a single formula.

For enterprise tasks, factual accuracy is not the only thing that matters: tone, safety, completeness, relevance and compliance with internal policies count too. In this approach, the judge model does not simply assign a score; it also helps explain why one answer is better than another. AWS stresses that this setup speeds up iteration: the team can see exactly where the model falls short and can adjust the reward function faster. That is especially useful in areas where an error does not look like an obvious bug but shows up in the nuances of wording, a missed risk or weak argumentation.

Six setup steps

AWS breaks the rollout of LLM-as-a-judge into several practical steps. First, you choose the evaluation type: rubric-based, where the judge gives an absolute score to a single answer, or preference-based, where it compares two options and picks the better one. If you have no ready-made preferences, the company recommends starting with the rubric approach and simple pass/fail criteria instead of a 1-to-10 scale.

• Choose the judging mode: absolute scoring or pairwise comparison
• Spell out quality criteria clearly, with observable signs
• Pick a judge model for your domain and budget through Amazon Bedrock
• Require a structured JSON response so the reward can be parsed reliably
• Tie the reward function to production metrics and add a resilient Lambda wrapper

AWS puts particular emphasis on infrastructure. It advises not relying on the judge alone and supplementing it with fast deterministic checks: JSON validity, response length, language match and safety filters. The Reward Lambda itself must handle thousands of evaluations per training step, so AWS recommends exponential backoff for Bedrock calls, parallelization via ThreadPoolExecutor or async patterns, a timeout of up to 15 minutes and provisioned concurrency of about 100 for typical configurations. If the judge or the API fails, it is better to return a neutral reward than to break the entire training step. AWS also advises keeping a set of regression tests for the judge pipeline itself.

The contract case

As a demonstration, AWS describes a project with a partner in the legal industry. The task is to automatically analyze new contracts, compare them with internal rules, past contracts and legal requirements, and output JSON with comments, types of issues and recommended actions.

The original dataset was small and contained expert-labeled contracts, so classic supervised fine-tuning had limited effect. For RFT, the team used a separate judge model, GPT OSS 120B, and a custom system prompt. The judge checked whether a comment actually rests on a passage from the contract itself, whether it is consistent with the reference document and whether action can be taken based on it. This was then wrapped in a Lambda function, and training was launched through the Nova Forge SDK with several generations per example and a concurrent-call limit of 100.

As a result, Amazon Nova 2 Lite after RFT reached an aggregate score of 4.33 out of 5 and perfect JSON schema validation, outperforming Claude Sonnet 4.5 and Claude Haiku 4.5. AWS separately notes that the SFT versions showed artifacts such as repeated comments and odd Unicode characters, while the RFT checkpoints did not. More important still, the model kept its good results even after the judge prompt was changed, meaning it learned general quality patterns rather than one specific scoring formula. The downside is also stated plainly: RFT required 4–8 rollouts per training example and cost more in compute.

What this means

AWS is effectively promoting RFT with LLM-as-a-judge as a workable path for tuning models for sensitive enterprise scenarios where simple rules are not enough and manual labeling is too expensive. If the Amazon Nova approach really carries over to production, companies in legal, finance and healthcare get a way to fine-tune models to their own standards while keeping tighter control over the format, quality and explainability of answers.