AWS explained how to fine-tune Amazon Nova through an LLM judge for complex enterprise tasks

AWS has detailed how to apply reinforcement fine-tuning with an LLM-as-a-judge approach for Amazon Nova models. Instead of manual annotation or a set of hard-coded rules, a separate language model evaluates answer quality, and its verdict becomes a reward signal for training.

Why a Judge Is Needed

According to AWS, standard RFT can be built either on verifiable rules like exact string matching, or on a scheme where another LLM evaluates the answer against multiple criteria at once. The second option is needed where quality cannot be reduced to a single formula. For corporate tasks, what matters is not only factual accuracy but also tone, safety, completeness, relevance, and compliance with internal policies.

In this approach, the judge model not only assigns a score but also helps explain why one answer is better than another. AWS emphasizes that this scheme accelerates iterations: teams see exactly where the model falls short and can fix the reward function faster. This is especially useful in domains where an error does not look like an obvious bug but manifests in nuances of phrasing, missed risk, or weak reasoning.

Six Steps to Configuration

AWS breaks down LLM-as-a-judge implementation into several practical steps. First, you must choose the evaluation type: rubric-based, where the judge assigns an absolute score to one answer, or preference-based, where it compares two options and picks the better one. If ready-made preferences don't exist, the company recommends starting with a rubric approach and simple pass/fail criteria instead of a 1–10 scale.

• Choose judging mode: absolute evaluation or pairwise comparison
• Clearly define quality criteria with observable indicators
• Select a judge model suited to your domain and budget through Amazon Bedrock
• Require structured JSON output so rewards can be reliably parsed
• Link the reward function to product metrics and add stable Lambda infrastructure

A separate emphasis is placed on infrastructure. AWS recommends not relying solely on the judge 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 exponential backoff for Bedrock calls, parallelization via ThreadPoolExecutor or async patterns, timeouts up to 15 minutes, and provisioned concurrency around 100 for typical configurations are recommended. If the judge or API fails, it is better to return a neutral reward than to break the entire training step. Additionally, teams should maintain a set of regression tests for the judge pipeline itself.

Contract Case Study

As a demonstration, AWS describes a project with a legal industry partner. The goal was to automatically analyze new contracts, compare them against internal rules, past contracts, and regulatory requirements, and output JSON with comments, remark types, and recommended actions. The initial dataset was small and contained expert-annotated contracts, so classical supervised fine-tuning produced limited results.

For RFT, they used a separate judge model GPT OSS 120B and a custom system prompt. The judge verified whether a comment actually relied on a fragment of the contract itself, whether it aligned with the reference document, and whether action could be taken based on it. They then wrapped this in a Lambda function and launched training through Nova Forge SDK with multiple generations per example and a concurrent call limit of 100.

As a result, Amazon Nova 2 Lite after RFT achieved an aggregated 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 SFT versions exhibited artifacts like repeated comments and strange Unicode characters, while RFT checkpoints did not. More importantly, the model maintained strong results even after changing the judge prompt, meaning it learned not a specific scoring formula but more general quality patterns.

The downside was also stated plainly: RFT required 4–8 rollouts per training example and was more expensive computationally.

What This Means

AWS is effectively promoting RFT with LLM-as-a-judge as a working approach for tuning models to sensitive corporate scenarios where simple rules are insufficient and manual annotation is too costly. If the Amazon Nova approach truly scales to production, companies in legal, finance, and healthcare sectors gain the ability to fine-tune models to their own standards while better controlling output format, quality, and explainability.