RePO: ReLU-based Preference Optimization
Authors: name: Junkang Wu, name: Kexin Huang, name: Xue Wang, name: Jinyang Gao, name: Bolin Ding, name: Jiancan Wu, name: Xiangnan He, name: Xiang Wang

Aligning large language models (LLMs) with human preferences is critical for
real-world deployment, yet existing methods like RLHF face computational and
stability challenges. While DPO establishes an offline paradigm with single
hyperparameter beta, subsequent methods like SimPO reintroduce complexity
through dual parameters (beta, gamma). We propose {ReLU-based Preference
Optimization (RePO)}, a streamlined algorithm that eliminates beta via two
advances: (1) retaining SimPO's reference-free margins but removing beta
through gradient analysis, and (2) adopting a ReLU-based max-margin loss that
naturally filters trivial pairs. Theoretically, RePO is characterized as
SimPO's limiting case (beta to infty), where the logistic weighting
collapses to binary thresholding, forming a convex envelope of the 0-1 loss.
Empirical results on AlpacaEval 2 and Arena-Hard show that RePO outperforms DPO
and SimPO across multiple base models, requiring only one hyperparameter to
tune.