Personalized Group Relative Policy Optimization for Heterogenous Preference Alignment

Personalized Group Relative Policy Optimization for Heterogenous Preference Alignment Despite their sophisticated general-purpose capabilities, Large Language Models (LLMs) often fail to align with diverse individual preferences because standard post-training methods, like Reinforcement Learning with Human Feedback (RLHF), optimize for a single, global objective. Policy stories matter because compliance friction can slow adoption even when model quality keeps improving.