arXiv AI June 30, 2026 By Zhennan Jiang, Shangqing Zhou, Yutong Jiang, Zefang Huang, Mingjie Wei, Yuhui Chen, Tianxing Zhou, Zhen Guo, Hao Lin, Quanlu Zhang, Yu Wang, Haoran Li, Chao Yu, Dongbin Zhao

WoVR: World Models as Reliable Simulators for Post-Training VLA Policies with RL

Read the original on arXiv AI →

arXiv:2602. 13977v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) promises to unlock capabilities beyond imitation learning for Vision--Language--Action (VLA) models, but its requirement for massive real-world interaction prevents direct deployment on physical robots.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 3

WorldSample: Closed-loop Real-robot RL with World Modelling

arXiv:2607. 02431v1 Announce Type: cross Abstract: Reinforcement learning (RL) can overcome the demonstration-coverage limitation of imitation learning (IL) by allowing robots to improve through trial-and-error interaction beyond the states observed in demonstrations.

By Yuquan Xue, Le Xu, Zeyi Liu, Zhenyu Wu, Zhengyi Gu, Xinyang Song, Bofang Jia, Ziwei Wang

reinforcement-learning robotics safety

arXiv AI

Aug 6

WorldCycle: Self-Verifiable Reinforcement Learning for Long-Horizon Video World Models

arXiv:2608. 04964v1 Announce Type: new Abstract: Interactive video world models are essential for long-horizon planning and exploration, yet they suffer from compounding errors.

By Bohai Gu, Yueyang Yuan, Taiyi Wu, Dazhao Du, Jian Liu, Xiaoyi Pang, Jie Zhang, Xiaocheng Lu, Haobin Zhong, Xiaotong Zhao, Alan Zhao, Song Guo

reinforcement-learning benchmarks

arXiv AI

Jun 9

BORA: Bridging Offline Reinforcement Learning and Online Residual Adaptation for Real-World Dexterous VLA Models

arXiv:2605. 30226v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm for grounding visual-language understanding into real-world robotic manipulation.

By Zhongxi Chen, Yifan Han, Yanming Shao, Huanming Liu, Congsheng Xu, Xiaoyu Chen, Yao Mu, Wenzhao Lian

reinforcement-learning robotics multimodal

arXiv Machine Learning

Jul 17

DriftWorld: Fast World Modeling through Drifting

arXiv:2607. 15065v1 Announce Type: cross Abstract: Predictive world models enable robots to plan by imagining the outcomes of their actions, but their value for control hinges on generating many rollouts quickly.

By Susie Lu, Haonan Chen, Weirui Ye, Yilun Du

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arXiv AI

Jun 30

Trust Your Instincts: Confidence-Driven Test-Time RL for Vision-Language-Action Models

arXiv:2606. 29892v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become indispensable for pushing Vision-Language-Action Models (VLAs) beyond static imitation learning.

By Siyao Chen, Jiakang Yuan, Jiaxin Wang, Tao Chen

reinforcement-learning multimodal benchmarks

arXiv AI

Jul 1

Z-1: Efficient Reinforcement Learning for Vision-Language-Action Models

arXiv:2606. 31846v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models offer a promising framework for robotic manipulation by connecting language instructions, visual observations, and continuous control.

By Lang Cao, Renhong Chen, Luyi Li, Peng Wang, Mofan Peng, Yitong Li

reinforcement-learning robotics fine-tuning multimodal benchmarks

Related stories

WorldSample: Closed-loop Real-robot RL with World Modelling

WorldCycle: Self-Verifiable Reinforcement Learning for Long-Horizon Video World Models

BORA: Bridging Offline Reinforcement Learning and Online Residual Adaptation for Real-World Dexterous VLA Models

DriftWorld: Fast World Modeling through Drifting

Trust Your Instincts: Confidence-Driven Test-Time RL for Vision-Language-Action Models

Z-1: Efficient Reinforcement Learning for Vision-Language-Action Models