[Submitted on 4 Sep 2026]
Title:Gradient-Aligned Pair Selection for Personalized Preference Optimization
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Abstract:Personalizing large language models (LLMs) requires aligning generation behavior with user-specific preferences rather than aggregate quality. While Direct Preference Optimization (DPO) provides a stable framework for preference learning, its effectiveness in personalized settings critically depends on how preference pairs are selected. Existing approaches typically rely on heuristic criteria, such as likelihood-based extremes, which decouple optimization from explicit user utility and can lead to degraded personalization. We formalize personalized preference learning as a geometry-aligned optimization problem by analyzing the first-order interaction between gradients of expected user utility and DPO update directions. Our analysis reveals that, under off-policy sampling, the DPO update transitions from a purely error-corrective signal to a reinforcement-like update when preference margins are directionally aligned with utility gradients. This perspective exposes pair selection as a geometric decision that governs whether preference optimization advances or hinders personalization. Motivated by this insight, we propose GAP-DPO (Geometry-Aligned Preference DPO), an iterative algorithm that performs utility-aware, geometry-aligned pair selection while controlling distribution shift via epoch-wise regeneration. Experiments on personalized text generation benchmarks show that GAP-DPO consistently improves stylistic fidelity, preference alignment, and generation quality compared to standard DPO variants. Together, our results establish gradient alignment as a unifying principle for personalized preference optimization and demonstrate that pair selection is an intrinsic component of the optimization geometry rather than a heuristic preprocessing step.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2610.00061 [cs.AI]
(or arXiv:2610.00061v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.00061
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Xinyu Li [view email] [v1] Fri, 4 Sep 2026 05:37:29 UTC (67 KB)
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