arXiv:2606. 09043v1 Announce Type: new Abstract: Reward models trained from pairwise preferences often exploit superficial shortcut cues rather than learning true response quality.
By Fengyuan Liu, Yongliang Miao, Zirui He, Yanguang Liu, Fei Sun, Mengnan Du
This paper studies what discriminatively trained reward models (RMs) memorize by measuring counterfactual memorization on two human preference datasets. We show that RMs 1) misallocate memorization to easy, high margin preference pairs, 2) memorize dataset-specific shortcuts (e.
arXiv:2607. 24484v1 Announce Type: new Abstract: This paper studies what discriminatively trained reward models (RMs) memorize by measuring counterfactual memorization on two human preference datasets.
By Ivo Verhoeven, Pushkar Mishra, Ekaterina Shutova
arXiv:2509. 22851v4 Announce Type: replace-cross Abstract: Margin-based optimization is fundamental to improving generalization and robustness in classification tasks.
By Yaswanth Chittepu, Prasann Singhal, Greg Durrett, Scott Niekum
arXiv:2603. 03291v2 Announce Type: replace-cross Abstract: Reward Models (RMs) are crucial for online alignment of language models (LMs) with human preferences.
By Daniel Fein, Max Lamparth, Violet Xiang, Mykel J. Kochenderfer, Nick Haber
arXiv:2604. 18239v4 Announce Type: replace-cross Abstract: Preference optimization is widely used to align large language models (LLMs) with human preferences.
By Wei Chen, Yubing Wu, Junmei Yang, Delu Zeng, Qibin Zhao, John Paisley, Min Chen, Zhou Wang