Preference learning in shades of gray: Interpretable and bias-aware reward modeling for human preferences

arXiv:2604.01312v1 Announce Type: new Abstract: Learning human preferences in language models remains fundamentally challenging, as reward modeling relies on subtle, subjective comparisons or shades of gray rather than clear-cut labels. This study investigates the limits of current approaches and pr