Predicting Human-Gain Curves under Local Proxy Optimization from Repeated Ratings

Authors: Moonwon Choi , Kisung Nam , Seunggeun Lee

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Abstract: Optimizing a proxy objective can improve human-perceived quality, but the resulting gain can vary with optimization strength. Average proxy-human correlation alone does not reveal how human-perceived quality changes as optimization strength increases. We propose PACE (Proxy Alignment and Curvature Expansion), a pre-optimization diagnostic that uses repeated human ratings to predict the human-gain curve induced by a fixed proxy. The method models proxy optimization as a KL-regularized exponential tilt. At KL radius $d$, the local gain is approximated by $A\sqrt{d}+Bd$, where $A$ measures first-order proxy-human alignment and $B$ captures the leading second-order correction along the proxy-optimization path. We estimate $A$ and $B$ from repeated human ratings on calibration items and test the resulting gain-curve prediction on held-out evaluation items. Across SummEval, analysis-eligible WMT MQM clusters, an OpenMEVA-MAGS ROC/WP repeated-rating slice, and a GPT-4o judge proxy experiment, PACE consistently reduces held-out gain-prediction error relative to $A$-only, zero-gain, and reliability-only baselines. With the same pre-pool information, it is competitive with flexible proxy-conditional calibration, and a LoRA-DPO experiment provides a modest bridge to actual optimization. Transfer experiments across datasets and evaluation slices show that source coefficients are useful priors, but target calibration further improves prediction. The resulting diagnostic is local and one-sided: it characterizes gentle optimization along the positive tilt branch of a fixed item pool.

Comments:

10-page main paper, 65 pages including references and supplementary material. Accepted as a Spotlight at NeurIPS 2026. Code: this https URL

Subjects:

Artificial Intelligence (cs.AI)

ACM classes:

I.2.6; I.2.7; G.3

Cite as:

arXiv:2610.41114 [cs.AI]

(or

arXiv:2610.41114v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.41114

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arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Moonwon Choi [ view email ]

[v1]

Fri, 30 Oct 2026 01:16:57 UTC (835 KB)

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