What Is RLCD? The Secret Behind Jev
A blog explains Jev's RLCD as calibrated multiway Plackett-Luce preference modeling rather than scalar rewards.
The post explains RLCD as a schema-conditioned Plackett-Luce objective that combines multiway preference modeling with probability calibration. It traces reward modeling from scalar scores to LLaMA-Berry's pairwise preference model, trained on about 7.8 million mathematical-solution pairs, and then to multi-candidate Luce choice. Jev is described as the product form, exposing the reward model through typed outputs rather than hiding it behind a generator. Calibration is framed with negative log loss and the Brier score so reported probabilities match observed accuracy.
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