Reinforcement Learning for Calibrated Decisions

Reinforcement Learning for Calibrated Decisions (RLCD) is a reinforcement learning training method tailored for decision and classification models. Unlike RLHF, which optimizes for human subjective preferences in conversational generation, RLCD focuses on calibrating probability distributions so that confidence scores strictly match empirical accuracy, enabling downstream software systems to set reliable threshold gates.