Labels or Preferences? Budget-Constrained Learning with Human Judgments over AI-Generated Outputs

• URL: http://arxiv.org/abs/2601.13458v1
• Date: Mon, 19 Jan 2026 23:23:29 GMT
• Title: Labels or Preferences? Budget-Constrained Learning with Human Judgments over AI-Generated Outputs
• Authors: Zihan Dong, Ruijia Wu, Linjun Zhang,
• Abstract summary: We show how to optimally allocate a fixed annotation budget between ground-truth labels and pairwise preferences in AI.<n>We introduce Preference-Calibrated Active Learning (PCAL), a novel robustness method that learns optimal data acquisition strategy.<n>This work provides a principled and statistically efficient approach for budget-constrained learning in modern AI.
• Score: 17.028710603629026
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The increasing reliance on human preference feedback to judge AI-generated pseudo labels has created a pressing need for principled, budget-conscious data acquisition strategies. We address the crucial question of how to optimally allocate a fixed annotation budget between ground-truth labels and pairwise preferences in AI. Our solution, grounded in semi-parametric inference, casts the budget allocation problem as a monotone missing data framework. Building on this formulation, we introduce Preference-Calibrated Active Learning (PCAL), a novel method that learns the optimal data acquisition strategy and develops a statistically efficient estimator for functionals of the data distribution. Theoretically, we prove the asymptotic optimality of our PCAL estimator and establish a key robustness guarantee that ensures robust performance even with poorly estimated nuisance models. Our flexible framework applies to a general class of problems, by directly optimizing the estimator's variance instead of requiring a closed-form solution. This work provides a principled and statistically efficient approach for budget-constrained learning in modern AI. Simulations and real-data analysis demonstrate the practical benefits and superior performance of our proposed method.

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