Daiwei Chen

PAL: Sample-Efficient Personalized Reward Modeling for Pluralistic Alignment

Foundation models trained on internet-scale data benefit from extensive alignment to human preferences before deployment. However, existing methods typically assume a homogeneous preference shared by all individuals, overlooking the diversity inherent in human values. In this work, we propose a gene…