What Is RLCD? The Secret Behind Jev

RLCD is a reward modeling framework that evolves from scalar rewards to multiway preference modeling combined with probability calibration, making the reward model explicit rather than hidden. Jev implements this by treating RLCD as a schema-conditioned Plackett-Luce objective with typed outputs and parallel inference, where the reward model becomes the actual model instead of operating behind a language generator. This advancement builds on earlier pairwise preference models like PPRM by shifting from absolute-looking scalar values to relative preference comparisons that have stable meaning across different problems and conditions.