  Offline preference optimization allows fine-tuning large models directly from
offline data, and has proved effective in recent alignment practices. We
propose generalized preference optimization (GPO), a family of offline losses
parameterized by a general class of convex functions. GPO enables a unified
view over preference optimization, encompassing existing algorithms such as
DPO, IPO and SLiC as special cases, while naturally introducing new variants.
The GPO framework also sheds light on how offline algorithms enforce
regularization, through the design of the convex function that defines the
loss. Our analysis and experiments reveal the connections and subtle
differences between the offline regularization and the KL divergence
regularization intended by the canonical RLHF formulation. In a controlled
setting akin to Gao et al 2023, we also show that different GPO variants
achieve similar trade-offs between regularization and performance, though the
optimal values of hyper-parameter might differ as predicted by theory. In all,
our results present new algorithmic toolkits and empirical insights to
alignment practitioners.
