TagPR: Tag-Guided Process Supervision for Personalization Reasoning in Large Language Models
TagPR introduces a new framework to improve how Large Language Models personalize reasoning by forcing them to write out their logic step-by-step. The method uses data-driven tagging and multi-stage reinforcement learning guided by a composite reward signal. This approach achieves state-of-the-art results on the LaMP benchmark and shows better performance on diverse, unseen tasks.