arXiv Machine Learning
Towards Bridging the Gap Between Offline and Iterative Alignment via Preference Distillation
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A Zeroth-Order Paradigm for LLM Preference Alignment
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Toward Preference-aligned Large Language Models via Residual-based Model Steering
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A Comprehensive Survey of Direct Preference Optimization: Datasets, Theories, Variants, and Applications
arXiv:2410. 15595v4 Announce Type: replace Abstract: With the rapid advancement of large language models (LLMs), aligning policy models with human preferences has become increasingly critical.
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Gradient-Aligned Pair Selection for Personalized Preference Optimization
The paper introduces GAP-DPO, a method for personalizing large language models by selecting preference pairs based on gradient alignment with user utility. It formalizes personalized preference learning as a geometry‑aligned optimization problem, showing that off‑policy sampling can shift DPO updates from error correction to reinforcement when preference margins align with utility gradients. Experiments demonstrate that GAP‑DPO improves stylistic fidelity, preference alignment, and overall generation quality over standard DPO variants.