Learning Heterogeneous Preferences
Learning from human feedback has become a central paradigm for training modern AI systems, where models of human utility are used as reward models in policy learning. Existing methods typically assume a \emph{universal utility} function shared across a population and treat disagreement between annotators as stochastic...
Shiwali Mohan, Matthew K. Hong, Du-Le Shu et al.
· 0 citations