Learning Preference Adaptation for Large Language Model Personalization via Verbal Reinforcement Learning
Natural language user preferences provide an interpretable interface for LLM personalization. However, universal preference summaries often contain information irrelevant to a particular downstream task. Directly supplying the full preference summary therefore wastes context capacity and introduces cross-task distraction, while manually designing task-specific preference views is difficult to scale. In this work, we study \emph{task-specific preference adaptation}: given a universal user preference summary and a downstream task, derive a task-conditioned representation that preserves sufficient decision-relevant evidence while removing redundant context. To this end, we propose \textsc{Al...
| Authors | Yuting Liu, Wei Wu, Jianzhe Zhao et al. |
|---|---|
| Primary category | cs.CL |
| Published | 10 August 2026 |
| Last updated at source | 12 August 2026 |
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