RePO: ReLU-based Preference Optimization

• URL: http://arxiv.org/abs/2503.07426v1
• Date: Mon, 10 Mar 2025 15:11:07 GMT
• Title: RePO: ReLU-based Preference Optimization
• Authors: Junkang Wu, Kexin Huang, Xue Wang, Jinyang Gao, Bolin Ding, Jiancan Wu, Xiangnan He, Xiang Wang,
• Abstract summary: We propose ReLU-based Preference Optimization (RePO), a streamlined algorithm that eliminates $beta$ via two advances.<n>RePO is characterized as SimPO's limiting case ($beta to infty$), where the logistic weighting collapses to binary thresholding.<n> Empirical results on AlpacaEval 2 and Arena-Hard show that RePO outperforms DPO and SimPO across multiple base models.
• Score: 47.87283407390014
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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