Reinforcement Fine-Tuning can use any reward signal, straightforward hand‑crafted rules (RLVR), or an LLM that evaluates model outputs (LLM-as-a-judge or RLAIF). RLAIF makes alignment far more flexible and powerful, especially when reward signals are vague and hard to craft manually. Unlike generic RFT rewards that rely on blunt numeric scoring like substring matching, an LLM judge reasons across multiple dimensions—correctness, tone, safety, relevance—providing context-aware feedback that captures subtleties and domain-specific nuances without task-specific retraining. Additionally, LLM judges offer built-in explainability through rationales (for example, “Response A cites peer-reviewed studies”), providing diagnostics that accelerate iteration, pinpoint failure modes directly, and reduce hidden misalignments, something static reward functions can’t do.

This section covers the key steps involved in designing and deploying LLM-as-a-judge reward functions.

The first critical decision is selecting your judge architecture. LLM-as-a-judge offers two primary evaluation modes: Rubric-based (point- based) judging and Preference-based judging, each suited to different alignment scenarios.

After you’ve selected your judge type, articulate the specific dimensions that you want to improve. Clear evaluation criteria are the foundation of effective RLAIF training.