Reinforcement fine-tuning with LLM-as-a-judge

This article explains how to use Reinforcement Fine-Tuning (RFT) with LLM-as-a-judge to align large language models for domain-specific tasks, using a legal contract review case study.

• RFT with LLM-as-a-judge (RLAIF) provides flexible, context-aware reward signals superior to manual rules
• Two judge architectures: rubric-based (absolute scoring) and preference-based (comparative evaluation)
• Six critical implementation steps: select judge architecture, define criteria, configure model, refine prompts, align with production metrics, build resilient Lambda function
• Combine LLM judges with deterministic checks (format validation, safety filters) for robust reward scoring
• Amazon Nova 2 Lite with RFT achieved 4.33 aggregate score, outperforming larger models like Claude Sonnet
• RFT eliminates training artifacts and generalizes well to modified evaluation criteria
• Higher compute costs than SFT justified for mission-critical applications requiring strong alignment

RFT with LLM-as-a-judge transforms base models into specialized, production-ready systems with superior alignment quality and explainability for complex domains.