CSPF: A Constrained Shared-Private Fusion Method for Non-Verifiable Preference Evaluation

Abstract:At present, reliable evaluation of non-verifiable tasks remains challenging. Existing approaches often fail to adequately capture the diverse evaluative criteria underlying human preferences in such tasks. To this end, we propose Constrained Shared-Private Fusion (CSPF), a fusion method that treats heterogeneous frozen reward models as complementary evaluators and learns to integrate their hidden-state representations under pairwise human-preference supervision. CSPF decomposes each expert signal into shared and expert-private representations, encouraging cross-expert alignment while preserving complementary viewpoints. Across experiments on LM-Arena target-domain adaptation and PPE out-of-distribution preference evaluation, CSPF achieves the best performance on the primary metrics among the evaluated single-expert reward-model, scalar-score multi-expert, and rubric-judge baselines. Overall, CSPF suggests that fusing hidden-state representations provides a more expressive basis for preference assessment, offering a practical route toward integrated evaluative signals for non-verifiable preference tasks.

Computation and Language (cs.CL)

arXiv:2607.20862 [cs.CL]

(or arXiv:2607.20862v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.20862 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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