arXiv:2504. 10796v4 Announce Type: replace-cross Abstract: Distributionally robust optimization (DRO) is widely used for decision-making under uncertainty, but its adversarial focus on worst-case loss can lead to overly conservative policies.
By Lukas-Benedikt Fiechtner, Jose Blanchet
arXiv:2503. 00539v2 Announce Type: replace-cross Abstract: Reinforcement learning from human feedback (RLHF) has evolved to be one of the main methods for fine-tuning large language models (LLMs).
By Debmalya Mandal, Paulius Sasnauskas, Goran Radanovic
arXiv:2606. 19117v1 Announce Type: cross Abstract: Offline policy learning has received growing attention in causal inference.
By Yiyan Huang, Cheuk Hang Leung, Qi Wu, Zhiheng Zhang
arXiv:2609.38938v1 Announce Type: new
Abstract: Reinforcement learning with human feedback (RLHF) learns from human comparisons, which can be corrupted or deliberately manipulated. This paper studies...
By Xinyi Ni, Lifeng Lai
arXiv:2606. 09073v1 Announce Type: cross Abstract: Reinforcement learning from human feedback (RLHF) is bottlenecked by \emph{reward hacking}, where the policy exploits errors in a proxy reward model (RM) and produces high RM scores without genuine quality gains.
By Ely Hahami, Yoel Zimmermann, Ray Zhou, Jack Benarroch Jedlicki
arXiv:2606. 01382v1 Announce Type: cross Abstract: Preference alignment is central to improving large language models, but standard reward-based formulations can be restrictive when human preferences are cyclic, non-transitive, or otherwise not representable by a scalar reward.
By Tianlong Nan, Xiaopeng Li, Christian Kroer, Tianyi Lin