RAWR: Reward Assignment Without Rollouts in Verifiable Domains
RAWR introduces Monte Carlo Net Information Gain (MCNIG) to automatically generate step-level labels for training Process Reward Models (PRMs). This method uses information theory to measure how much each reasoning step contributes to the correct final answer, achieving linear complexity and significantly outperforming prior labeling techniques across math, code, and SQL benchmarks.