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:2604. 17415v3 Announce Type: replace-cross Abstract: Reward-based fine-tuning steers a pretrained diffusion or flow-based generative model toward higher-reward samples while remaining close to the pretrained model.
By Jeongjae Lee, Jinho Chang, Jeongsol Kim, Jong Chul Ye
The paper introduces CurriPO, a tree‑structured curriculum that automatically adapts to diverse user reward models in AI alignment tasks. By exploiting the natural hierarchy between easy‑ and hard‑to‑optimize reward models, CurriPO covers a broad user population in a single traversal, reusing previously incorporated reward models. Experiments on personalized continuous control show that CurriPO improves population satisfaction by 1.2–2.1× over the strongest baseline while cutting training time and better serving users traditionally underserved by conventional optimization.
By Taehyung Kim, Jongeun Choi
The paper introduces CurriPO, a tree‑structured curriculum that adapts to diverse user reward models in AI alignment. By automatically building a curriculum that branches and reuses reward models, it addresses the problem of users whose reward models are hard to optimize, a group often underserved by conventional methods. Experiments on personalized continuous control demonstrate that CurriPO improves population satisfaction by 1.2–2.1× over the best baseline while cutting training time.
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
arXiv:2506. 13702v4 Announce Type: replace-cross Abstract: Single-trajectory preference optimization methods learn from datasets of ((prompt, response, reward)) tuples, offering a practical alternative to pairwise preference learning by directly leveraging scalar feedback.
By Bilal Faye, Hanane Azzag, Mustapha Lebbah