arXiv:2602. 09533v2 Announce Type: replace Abstract: Direct preference optimization (DPO) has emerged as a promising approach for aligning large language models (LLMs) with human preferences.
By Masanari Oi, Mahiro Ukai, Masahiro Kaneko, Naoaki Okazaki, Nakamasa Inoue
arXiv:2502.14643v3 Announce Type: replace
Abstract: Direct Preference Optimization (DPO) is a widely adopted offline algorithm for preference-based reinforcement learning from human feedback (RLHF),...
By Gengxu Li, Tingyu Xia, Yi Chang, Yuan Wu
arXiv:2606. 19744v1 Announce Type: cross Abstract: Aligning language models with human preferences often requires optimising multiple behavioural objectives.
By Pranav Bhandari, Nicolas Fay, Amitava Datta, Usman Naseem, Mehwish Nasim
arXiv:2510. 05342v2 Announce Type: replace-cross Abstract: Direct Preference Optimization (DPO) has emerged as a simple and effective method for aligning large language models.
By Hyung Gyu Rho
arXiv:2410. 15595v4 Announce Type: replace Abstract: With the rapid advancement of large language models (LLMs), aligning policy models with human preferences has become increasingly critical.
By Wenyi Xiao, Zechuan Wang, Leilei Gan, Shuai Zhao, Zongrui Li, Ruirui Lei, Wanggui He, Luu Anh Tuan, Long Chen, Hao Jiang, Zhou Zhao, Fei Wu
The paper investigates the Direct Preference Optimization (DPO) objective used for aligning language models, revealing that its coefficient β simultaneously controls both the inverse preference-noise scale and the optimization dynamics. This entanglement causes non‑monotonic policy deviation with respect to β and makes loss values incomparable across different β settings. The authors propose a centered‑softplus reformulation that decouples these effects, allowing independent tuning of the noise scale and learning‑rate, and provides a smooth β←0 limit that reduces to a linear preference‑margin objective.
By Ivan Kruzhilov