Using continuous probability-based scores instead of discrete LLM judgments improves verification accuracy and calibration, and these fine-grained signals can guide both solution selection and reinforcement learning training.
This paper introduces LLM-as-a-Verifier, a framework that uses language models to evaluate solution correctness by computing probability distributions over scoring tokens rather than discrete scores.