arXiv AI

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

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.

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

23h ago

Q-Learning With World Models

arXiv:2608. 17163v1 Announce Type: cross Abstract: Off-policy reinforcement learning (RL) has become increasingly sample-efficient, enabling applications such as RL fine-tuning of Vision-Language-Action models into reliable, high-performing policies.

By Perry Dong, Yueru Jia, Chelsea Finn, Dorsa Sadigh

reinforcement-learning robotics fine-tuning multimodal benchmarks 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

diffusion robotics benchmarks

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

arXiv Machine Learning

Jun 29

Support-Constrained RL Enables Real-World Policy Improvement without Real-World Experience

arXiv:2606. 27475v1 Announce Type: cross Abstract: Robots trained on real world data tend to be imprecise, slow, and brittle to perturbations.

By Raymond Yu, William Huey, Mustafa Mukadam, Anusha Nagabandi, Abhishek Gupta

reinforcement-learning robotics efficiency

arXiv AI

Jun 16

LaWAM: Latent World Action Models for Efficient Dynamics-Aware Robot Policies

arXiv:2606. 15768v1 Announce Type: cross Abstract: Vision-Language-Action models (VLAs) leverage large-scale vision-language pretraining for semantic robot control, but often lack explicit foresight into how robot actions change the scene.

By Jialei Chen, Kai Wang, Kang Chen, Shuaihang Chen, Feng Gao, Wenhao Tang, Zhiyuan Li, Weilin Liu, Zhuyu Yao, Boxun Li, Yuanbo Xu, Chao Yu

diffusion robotics multimodal benchmarks

arXiv AI

Jul 7

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models

arXiv:2607. 04265v1 Announce Type: cross Abstract: World-action (WA) models can generate long-horizon action chunks for general-purpose robotic manipulation, but they remain vulnerable to calibration, perception, and contact-dynamics errors in real-world precision tasks, often failing in the final few millimeters of alignment or insertion.

By Angen Ye, Weijie Ke, Xiaofeng Wang, Xinze Chen, Chaojun Ni, Guosheng Zhao, Boyuan Wang, Zheng Zhu, Junjie Xie, Dapeng Zhang

reinforcement-learning robotics safety

arXiv AI

23h ago

EXPO-FT: Sample-Efficient Reinforcement Learning Finetuning for Vision-Language-Action Models

arXiv:2605. 25477v2 Announce Type: replace-cross Abstract: The ability to efficiently and reliably learn new tasks has been a foundational challenge in robotics.

By Perry Dong, Kuo-Han Hung, Tian Gao, Dorsa Sadigh, Chelsea Finn

reinforcement-learning robotics fine-tuning multimodal

arXiv AI

Jul 2

From World Models to World Action Models: A Concise Tutorial for Robotics

arXiv:2607. 00836v1 Announce Type: cross Abstract: World models are increasingly used in embodied intelligence and generative simulation, yet their scope remains ambiguous across communities.

By Xiaoxiong Zhang, Xiong Zeng, Wei Zhang

robotics safety

Related stories