Swap-guided Preference Learning for Personalized Reinforcement Learning from Human Feedback

Researchers propose Swap-guided Preference Learning (SPL) to address posterior collapse issues in Variational Preference Learning for RLHF systems. SPL introduces three new components to better capture personalized user preferences and improve AI alignment with diverse human values.