arXiv AI

Elastic Queries Reinforcement Learning: Self-Aware Policy Execution for VLA Models

arXiv:2606. 14375v1 Announce Type: cross Abstract: Vision-language-action (VLA) models are powerful action generators for robot manipulation, but they are typically executed with fixed inference and replanning schedules.

arXiv AI

Jun 11

Vision-Language-Action Jump-Starting for Reinforcement Learning Robotic Agents

arXiv:2604. 13733v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) enables high-frequency, closed-loop control for robotic manipulation, but scaling to long-horizon tasks with sparse or imperfect rewards remains difficult due to inefficient exploration and poor credit assignment.

By Angelo Moroncelli, Roberto Zanetti, Marco Maccarini, Loris Roveda

agents reinforcement-learning robotics efficiency multimodal safety

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 Machine Learning

Jul 9

Latent Policy Steering through One-Step Flow Policies

arXiv:2603. 05296v2 Announce Type: replace-cross Abstract: Offline reinforcement learning (RL) allows robots to learn from offline datasets without risky exploration.

By Hokyun Im, Andrey Kolobov, Jianlong Fu, Youngwoon Lee

reinforcement-learning robotics efficiency 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 AI

Jun 17

PearlVLA: Progressive Embodied Action-Plan Refinement in Latent Space

arXiv:2606. 17924v1 Announce Type: cross Abstract: Current Vision-Language-Action (VLA) models face a trade-off between efficient action generation and explicit deliberation.

By Bochen Yang, Lianlei Shan

llms diffusion reinforcement-learning robotics multimodal benchmarks

arXiv AI

1d ago

Algorithm-Architecture Co-Design for Efficient VLA Inference via Speculative Inference and Verification

arXiv:2608. 15636v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated remarkable capabilities in the field of embodied AI, but their high computational cost and limited predicted action length hinder real-time deployment.

By Chunyu Qi, Zhuoran Song, Jian Weng, Haozhe Jiang, Xueyuan Liu, Naifeng Jing, Guanghui He, Xiaoyao Liang, Haibing Guan

robotics efficiency multimodal benchmarks

arXiv AI

Jun 4

DEFLECT: Temporal Counterfactual Preference Learning for Delay-Robust Asynchronous VLAs

arXiv:2605. 19294v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) policies increasingly rely on asynchronous inference to hide large-model latency behind ongoing robot motion.

By Yixiang Zhu, Yonghao Chen, Zijie Yang, Yusong Hu, Xinyu Chen

robotics fine-tuning multimodal safety

arXiv AI

Aug 6

Explicit Language Memory for Long-Horizon Planning in Vision-Language-Action Models

arXiv:2608. 04765v1 Announce Type: cross Abstract: Vision-language-action (VLA) models provide a unified paradigm for connecting visual perception, language understanding, and robotic control.

By Houze Xu, Jizhong Li, Ziyi Ye

llms nlp robotics fine-tuning multimodal

arXiv AI

Jun 10

RoboGPT-R1: Enhancing Robot Task Planning with Reinforcement Learning

arXiv:2510. 14828v3 Announce Type: replace Abstract: Improving the reasoning capabilities of embodied agents is crucial for robots to complete complex human instructions in long-view manipulation tasks successfully.

By Jinrui Liu, Bingyan Nie, Boyu Li, Yaran Chen, Yuze Wang, Shunsen He, Haoran Li

llms agents reinforcement-learning robotics fine-tuning benchmarks

arXiv Machine Learning

Jun 16

AVA-VLA: Improving Vision-Language-Action models with Active Visual Attention

arXiv:2511. 18960v4 Announce Type: replace Abstract: Vision-Language-Action (VLA) models have shown remarkable progress in embodied tasks recently, but most methods process visual observations independently at each timestep.

By Lei Xiao, Jifeng Li, Juntao Gao, Feiyang Ye, Yan Jin, Jingjing Qian, Jing Zhang, Yong Wu, Xiaoyuan Yu

agents reinforcement-learning robotics multimodal benchmarks

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

Jun 24

LaGO: Latent Action Guidance for Online Reinforcement Learning

arXiv:2606. 24669v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong potential for planning and sequential decision-making, but prior work often relies on using them as direct controllers, which requires precise action generation and can be unreliable in practice.

By Kuan-Yen Liu, Ren-Jyun Huang, Ti-Rong Wu

llms reinforcement-learning robotics benchmarks

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