Preference Drift

Preference drift, the phenomenon of changing user preferences over time, poses a significant challenge for machine learning systems designed to align with human desires. Current research focuses on developing algorithms and models, such as dynamic Bradley-Terry models and reinforcement learning methods incorporating preference matching regularization, that explicitly account for this temporal instability in preference optimization. Addressing preference drift is crucial for improving the robustness and reliability of AI systems across various applications, from large language models and recommender systems to resource allocation in dynamic environments. This research aims to create more adaptable and user-centric systems that can effectively navigate evolving preferences.