Weitao Lu

This work presents CARD, a hierarchical framework that achieves effective personalization through progressive refinement that first clusters users according to shared stylistic patterns and learns group-specific LoRA adapters, enabling robust generalization and strong low-resource performance.

Listwise preference optimization for diffusion models (Diffusion-LPO) beats pairwise DPO baselines, finally unlocking the potential of richer ranked human feedback.