arXiv AI
Jun 19

AAPA: Adversarially Anchored Preference Alignment for Post-Training of Large Language Models

arXiv:2509. 25148v2 Announce Type: replace Abstract: Post-training alignment of large language models often combines supervised fine-tuning (SFT) on expert demonstrations with reinforcement learning (RL) from preference or verifiable feedback.

By Faqiang Qian, Kang An, Weikun Zhang, Ziliang Wang, Xuhui Zheng, Liangjian Wen, Yong Dai, Mengya Gao, Yichao Wu
arXiv AI
Jun 2

From Demonstrations to Rewards: Test-Time Prompt Optimization for VLM Reward Models

arXiv:2606. 00083v1 Announce Type: cross Abstract: Reinforcement learning relies on accurate reward functions, which are often hand-crafted or even unavailable in real-world applications, such as robotics.

By Christian Gumbsch, Leonardo Barcellona, Lennard Sch\"unemann, Platon Karageorgis, Andrii Zadaianchuk, Zehao Wang, Sergey Zakharov, Fabien Despinoy, Rahaf Aljundi, Efstratios Gavves
arXiv Machine Learning
Jul 8

Supervised Reward Inference

arXiv:2502. 18447v2 Announce Type: replace Abstract: Existing approaches to reward inference typically assume that humans provide demonstrations according to specific behavior models.

By Will Schwarzer, Jordan Schneider, Philip S. Thomas, Scott Niekum