GoKawiilExplainer details RLCD, the reward-modeling technique underlying Jev
A technical write-up describes RLCD (Reinforcement Learning from Calibrated Decisions) as an evolution of reward modeling, moving from scalar reward scores to pairwise preference models like PPRM, and finally to a schema-conditioned, multiway probability distribution. It explains that Jev implements this RLCD objective by adding typed outputs and parallel inference, effectively turning the reward model itself into the primary decision-making system rather than a hidden scoring layer behind a generator.