arXiv:2608.30378v1 Announce Type: cross
Abstract: Direct vision-language-action policies generate continuous robot actions efficiently, but standard behavior cloning leaves two complementary gaps: th...
By Botong Zhao, Fang Yu, Tim, Senhua Zhu, Xinyuan Chen, Yue Lu
arXiv:2609.34426v2 Announce Type: replace
Abstract: This paper addresses the problem of safe offline reinforcement learning, which involves training a policy to satisfy safety constraints using an of...
By Shengchao Hu, Peng Wang, Jifeng Hu, Qiyang Zhou, Anning Hu, Li Shen, Ya Zhang, Dacheng Tao
arXiv:2608.30122v1 Announce Type: cross
Abstract: Vision-language-action (VLA) driving methods increasingly combine multi-trajectory imitation learning with group-relative policy optimization (GRPO),...
By Tian Zhang, Zhuo Huang, Hongrui Ye, Yu Wu, Zengmao Wang, Kaixuan Zhou
arXiv:2607. 07859v1 Announce Type: new Abstract: Reinforcement learning (RL) research has increasingly shifted focus towards alignment, ensuring agents learn behaviors adhering to human values.
By Benjamin Poole, Minwoo Lee
arXiv:2608. 10634v1 Announce Type: new Abstract: Model-based reinforcement learning (MBRL), which learns environment dynamics to generate synthetic experience, is a promising approach to sample-efficient decision making.
By Zefeng Liang, Jie Qiao, Ruichu Cai, Weilin Chen, Zhifeng Hao
arXiv:2609.06100v1 Announce Type: cross
Abstract: Verifiable outcome rewards guide language-model post-training, but sequence-level advantages do not identify which token-level decisions should be pr...
By Haijiang Li, Chengyu Lv, Yi Zhang, Zhibing Zhang, Rui Qian, Yuchen Zhang, Xiaofan Zhang, Mingshan Wang, Xiaofei Jing, Yu Tong, Cangqi Zhou