Text as a Universal Interface for Transferable Personalization

• URL: http://arxiv.org/abs/2601.04963v1
• Date: Thu, 08 Jan 2026 14:09:17 GMT
• Title: Text as a Universal Interface for Transferable Personalization
• Authors: Yuting Liu, Jian Guan, Jia-Nan Li, Wei Wu, Jiang-Ming Yang, Jianzhe Zhao, Guibing Guo,
• Abstract summary: We advocate natural language as a universal, model- and task-agnostic interface for preference representation.<n>We introduce a two-stage training framework that combines supervised fine-tuning on high-quality synthesized data with reinforcement learning.<n>We develop AlignXplore+, a universal preference reasoning model that generates textual preference summaries.
• Score: 23.403737087576342
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study the problem of personalization in large language models (LLMs). Prior work predominantly represents user preferences as implicit, model-specific vectors or parameters, yielding opaque ``black-box'' profiles that are difficult to interpret and transfer across models and tasks. In contrast, we advocate natural language as a universal, model- and task-agnostic interface for preference representation. The formulation leads to interpretable and reusable preference descriptions, while naturally supporting continual evolution as new interactions are observed. To learn such representations, we introduce a two-stage training framework that combines supervised fine-tuning on high-quality synthesized data with reinforcement learning to optimize long-term utility and cross-task transferability. Based on this framework, we develop AlignXplore+, a universal preference reasoning model that generates textual preference summaries. Experiments on nine benchmarks show that our 8B model achieves state-of-the-art performanc -- outperforming substantially larger open-source models -- while exhibiting strong transferability across tasks, model families, and interaction formats.

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