LoRe: Personalizing LLMs via Low-Rank Reward Modeling

• URL: http://arxiv.org/abs/2504.14439v1
• Date: Sun, 20 Apr 2025 01:16:24 GMT
• Title: LoRe: Personalizing LLMs via Low-Rank Reward Modeling
• Authors: Avinandan Bose, Zhihan Xiong, Yuejie Chi, Simon Shaolei Du, Lin Xiao, Maryam Fazel,
• Abstract summary: We introduce a novel framework that leverages low-rank preference modeling to efficiently learn and generalize user-specific reward functions.<n>We validate our method on multiple preference datasets, demonstrating superior generalization to unseen users and improved accuracy in preference prediction tasks.
• Score: 47.12507639759984
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Personalizing large language models (LLMs) to accommodate diverse user preferences is essential for enhancing alignment and user satisfaction. Traditional reinforcement learning from human feedback (RLHF) approaches often rely on monolithic value representations, limiting their ability to adapt to individual preferences. We introduce a novel framework that leverages low-rank preference modeling to efficiently learn and generalize user-specific reward functions. By representing reward functions in a low-dimensional subspace and modeling individual preferences as weighted combinations of shared basis functions, our approach avoids rigid user categorization while enabling scalability and few-shot adaptation. We validate our method on multiple preference datasets, demonstrating superior generalization to unseen users and improved accuracy in preference prediction tasks.

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