arXiv AI

ValueFormer: A Causal Transformer Value Function with Stage-Aware Labels for Semi-Autonomous Vision-Language-Action Policies

arXiv:2608. 02958v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) policies trained by behavior cloning fail silently: from the action stream alone, a collapsing rollout looks much like one making clean progress, because imitation supplies no notion of progress.

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

arXiv Machine Learning

Jul 13

Learning More from Less: Reinforcement Learning from Hindsight

arXiv:2607. 09042v1 Announce Type: new Abstract: Reinforcement learning (RL) is increasingly used to post-train vision-language-action (VLA) models, but every update consumes robot rollouts that are slow and costly to collect, making sample efficiency a central concern.

By Iris Xu, Sunshine Jiang, John Marangola, Nitish Dashora, Richard Li, Thomas Liu, Zexue He, Yuheng Zhi, Alex Pentland, Pulkit Agrawal, Zhang-Wei Hong

llms reinforcement-learning robotics multimodal

Hugging Face Trending Papers

Jul 20

Patch Policy: Efficient Embodied Control via Dense Visual Representations

Pretrained dense visual features from Vision Transformers (ViTs) are powerful yet have been underutilized in robot learning. Modern robot policies either compress each observation into a single global token, or rely on visual backbones trained from scratch, sacrificing both fine-grained spatial detail and the benefits of large-scale visual pre-training.

llms robotics fine-tuning 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

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

6d ago

G0.5: One Autoregressive Stream for Robot Reasoning and Action

arXiv:2608. 11739v1 Announce Type: cross Abstract: The prevailing recipe for Vision-Language-Action (VLA) models couples a pretrained VLM with a separately trained flow-matching action expert.

By Yicheng Liu, Zibin Dong, Baijun Ye, Tianyuan Yuan, Tao Jiang, Anqi Yang, Shicheng Cao, Haonan Liu, Yue Sun, Zihan Guo, Xiao Liu, Dong Ke, Changxun Pan, Chenru Wu, Tailai Cheng, Xiaoshu Ren, Xinlei Zhang, Jianning Cui, Zijie Zhao, Haoyu Zhang, Kaiming Xu, Haodong Yang, Bowen Zhang, Jiahui Niu, Shaoting Zhu, Shiduo Zhang, Hang Zhao

llms nlp robotics fine-tuning multimodal benchmarks

arXiv Machine Learning

23h ago

Prism-GRPO: Faster VLA Policy Optimization via Splitting Same-outcome Groups

arXiv:2608. 17423v1 Announce Type: cross Abstract: GRPO is increasingly used for reinforcement learning of vision-language-action (VLA) policies because, unlike PPO, it does not require training a critic.

By Zeyun Deng, Yuzhe Lu, Yawei Wang, Linbo Liu, Qing Ping, Han Ding, Guande Wu, Panpan Xu, Jun Huan

reinforcement-learning robotics multimodal safety

arXiv AI

Aug 5

Continue or Replan? Bernoulli-Continuation Policy Learning for Adaptive Horizon Execution

arXiv:2608. 03483v1 Announce Type: cross Abstract: Existing chunk-based Vision-Language-Action (VLA) models execute a fixed number of actions (i.

By Weichen Xu, Zhenhua Liu, Lin Luo, Yaobo Liang, Chengtang Yao, Qingyu Mei, Jian Cao, Xixin Cao, Xing Zhang, Jiaolong Yang, Baining Guo

reinforcement-learning robotics multimodal safety

arXiv AI

Jun 9

ReCoVLA: VLM-Guided Reward Compilation for Failure Recovery in Vision-Language-Action Policies

arXiv:2606. 09630v1 Announce Type: cross Abstract: Vision-language-action (VLA) policies provide strong priors for language-conditioned manipulation, but remain brittle in off-nominal states requiring targeted recovery.

By Haodi Hu, Chung-Ta Huang, Jing Liu, Ye Wang, Kei Suzuki, Matthew Brand, Toshiaki Koike-Akino

llms robotics fine-tuning multimodal

arXiv AI

Jun 9

Benchmarking Vision-Language-Action Models on SO-101: Failure and Recovery Analysis

arXiv:2606. 08881v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated strong generalization in robotic manipulation, yet existing evaluations are primarily conducted in simulation or on expensive robotic platforms, leaving their robustness on affordable real-world robots largely unexplored.

By Yi Yu, Xinchuan Qiu

robotics fine-tuning multimodal benchmarks

arXiv AI

Jun 3

PHASER: Phase-Aware and Semantic Experience Replay for Vision-Language-Action Models

arXiv:2606. 03598v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have achieved remarkable success in language-conditioned robotic manipulation.

By Ziyang Chen, Shaoguang Wang, Weiyu Guo, Qianyi Cai, He Zhang, Pengteng Li, Yiren Zhao, Yandong Guo

agents robotics multimodal

arXiv AI

Jun 30

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.

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

reinforcement-learning robotics safety

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