arXiv Machine Learning June 15, 2026 By Chengxuan Lu, Shukuan Wang, Yanjie Li, Yingying Fang, Huoyan Wang, Tian Zhang, Wei Liu, Shiji Jin, Fuyuan Qian, Peiming Li, Chao Xu, Baigui Sun, Yang Liu

AcceRL: A Distributed Asynchronous Reinforcement Learning and World Model Framework for Vision-Language-Action Models

Read the original on arXiv Machine Learning →

arXiv:2603. 18464v3 Announce Type: replace Abstract: Reinforcement learning (RL) for large-scale Vision-Language-Action (VLA) models is severely bottlenecked by synchronization barriers and the high cost of environment data acquisition.

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

arXiv Machine Learning

Jun 9

C$^3$ache: Accelerating World Action Models with Cross Inference Chunk Cache

arXiv:2606. 08962v1 Announce Type: new Abstract: World Action Models (WAMs) generalize better than standard Vision-Language-Action (VLA) policies to novel motions and environments, because a video-modeling objective lets them learn from abundant unlabeled video rather than scarce labeled robot demonstrations.

By Weisen Zhao, Lam Nguyen, Zhicong Lu, Yuzhang Shang

robotics multimodal benchmarks

arXiv Machine Learning

Aug 10

Fast and Accurate: An Adaptive VLA Inference Framework through Environment-aware Model Selection

arXiv:2608. 06434v1 Announce Type: cross Abstract: Embodied intelligence demands both long-horizon reasoning and real-time closed-loop responsiveness.

By Yuewei Sun, Lang Qin, Zechuan Tian, Jingwen Li, Guiqin Wang, Shengzeng Huo, Wenxin Ren, Tao Fang, Xiaochen Zhang, Guanqing Deng, Xiang Wang, Xiaowen Dong, Qinghai Guo, Yuxin Ma

robotics efficiency multimodal benchmarks

arXiv AI

Jul 28

VLASH: Real-Time VLAs via Future-State-Aware Asynchronous Inference

arXiv:2512. 01031v2 Announce Type: replace-cross Abstract: Vision-Language-Action models (VLAs) are becoming increasingly capable across diverse robotic tasks.

By Jiaming Tang, Yufei Sun, Yilong Zhao, Shang Yang, Yujun Lin, Zhuoyang Zhang, James Hou, Yao Lu, Zhijian Liu, Song Han

robotics efficiency multimodal benchmarks

arXiv Machine Learning

Jul 7

XS-VLA: Coupling Coarse-grained Spatial Distillation with Latent Flow Matching for Lightweight Robotic Control

arXiv:2607. 04171v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have shown strong multimodal understanding and spatial grounding, but their computational cost limits real-time robotic control.

By Lei Iok Tong, Qingchen Xie, Wei Huang, Ying Jie Yap, Yujie Zhang, Qianzhi Li, Xiaolong Liu, Zhidong Deng

llms diffusion robotics fine-tuning efficiency multimodal benchmarks

arXiv Machine Learning

Jul 8

Training-Free Acceleration for Vision-Language-Action Models with Action Caching and Refinement

arXiv:2607. 06370v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have emerged as a promising approach for generalizable robotic manipulations.

By Ryuji Oi, Hikari Otsuka, Kosuke Matsushima, Yuki Ichikawa, Masato Motomura, Tatsuya Kaneko, Daichi Fujiki

diffusion robotics efficiency multimodal

arXiv Machine Learning

Jul 21

Patch Policy: Efficient Embodied Control via Dense Visual Representations

arXiv:2607. 18236v1 Announce Type: cross Abstract: Pretrained dense visual features from Vision Transformers (ViTs) are powerful yet have been underutilized in robot learning.

By Gaoyue Zhou, Zichen Jeff Cui, Ada Langford, Bowen Tan, Yann LeCun, Lerrel Pinto

llms robotics fine-tuning efficiency multimodal benchmarks

Related stories

C$^3$ache: Accelerating World Action Models with Cross Inference Chunk Cache

Fast and Accurate: An Adaptive VLA Inference Framework through Environment-aware Model Selection

VLASH: Real-Time VLAs via Future-State-Aware Asynchronous Inference

XS-VLA: Coupling Coarse-grained Spatial Distillation with Latent Flow Matching for Lightweight Robotic Control

Training-Free Acceleration for Vision-Language-Action Models with Action Caching and Refinement

Patch Policy: Efficient Embodied Control via Dense Visual Representations