What Is RLCD? Fudan Researcher Explains the Method Behind Jev
SinclairWang1 · x · 2026-09-22
Fudan University PhD candidate Di Zhang published an explainer of RLCD, the method behind Jev, framing it as the next step in reward modeling rather than a mysterious language-model alternative.
Core idea
- RLCD = multiway preference modeling + probability calibration.
- Concretely, it is a schema-conditioned Plackett–Luce objective; Jev turns that objective into a product with typed outputs and parallel inference.
Argument
- Conventional reward models (ORM/PRM) output a scalar r(x,a), but the number isn't absolute: 0.8 has no stable meaning across problems, candidate pools, checkpoints, or model families—it only supports comparisons under similar conditions.
- The learned object evolves from a scalar reward to a preference, then to a multiway distribution, and calibration turns that distribution into a decision interface.
- Key takeaway: the reward model is no longer hidden behind a generator—the reward model becomes the model.
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