arXiv AI July 23, 2026 By Dmitriy Poyarkov, Aleksei Staroverov, Aleksandr I. Panov

Leveraging Offline Supervision for Efficient and Generalizable Reinforcement Learning in Large-Scale Vision-Language-Action Models

arXiv:2607. 19399v1 Announce Type: cross Abstract: It is commonly observed that online reinforcement learning (RL) produces better-performing strategies than offline methods across a broad range of performance measures.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Aug 24

Decoupling Policy Extraction for Offline Reinforcement Learning

arXiv:2608.20909v1 Announce Type: new Abstract: Offline RL methods commonly jointly train the actor and critic, where the critic is used to guide the actor toward higher-value actions. This coupled l...

By Xuyao Lin, Yixiang Shan, Jinru Duan, Tao Yang, Xinyu Zhao, Runyu Lei, Yiming Zhao, Jiaxin Fan, Zongbao Feng, Peng Jia

rag reinforcement-learning

arXiv AI

Sep 23

RankQ: Offline-to-Online Reinforcement Learning via Self-Supervised Action Ranking

arXiv:2605.11151v3 Announce Type: replace Abstract: Offline-to-online reinforcement learning (RL) improves sample efficiency by leveraging pre-collected datasets prior to online interaction. A key ch...

By Andrew Choi, Wei Xu

reinforcement-learning robotics fine-tuning 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 AI

Jul 14

Active Offline-to-Online Reinforcement Learning

arXiv:2607. 11720v1 Announce Type: cross Abstract: Background: Offline reinforcement learning (RL) enables effective policies to be trained from large, previously collected datasets and subsequently improved through limited online interaction.

By Alper Kamil Bozkurt, Shangtong Zhang, Yuichi Motai

reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 16

Efficient Reinforcement Learning by Guiding World Models with Non-Curated Data

arXiv:2502. 19544v3 Announce Type: replace Abstract: Leveraging offline data is a promising way to improve the sample efficiency of online reinforcement learning (RL).

By Yi Zhao, Aidan Scannell, Wenshuai Zhao, Yuxin Hou, Tianyu Cui, Le Chen, Dieter B\"uchler, Arno Solin, Juho Kannala, Joni Pajarinen

reinforcement-learning robotics fine-tuning

arXiv AI

Sep 7

VLA-Precision: Asymmetric Co-Bootstrapping for Efficient Real-World Online RL of Vision-Language-Action Models

VLA-Precision introduces an efficient real‑world online reinforcement learning framework for vision‑language‑action (VLA) models, featuring the Asymmetric Co‑Bootstrapping (ACoB) algorithm and the ACoB‑Stream architecture. ACoB uses asymmetric co‑bootstrapping across timescales to rapidly improve policy performance while refining value estimates, thereby reducing policy drift. ACoB‑Stream enables large VLA models to run with up to 10.9× higher throughput and computational efficiency, achieving a 98.3 % mean success rate on nine high‑precision chemistry tasks in under 46 minutes per task.

By Chenyu Su, Zhaolong Shen, Yuan Qian, Chen Qian, Rui Zhang, Feng Yan, Weixing Chen, Fei Zhang, Jiamin Wang, Shuang Cong, Weiwei Shang

agents reinforcement-learning robotics efficiency multimodal

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Decoupling Policy Extraction for Offline Reinforcement Learning

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