arXiv AI June 11, 2026 By Angelo Moroncelli, Roberto Zanetti, Marco Maccarini, Loris Roveda

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.

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

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 7

Simple-to-Complex Structured Demonstrations for Vision-Language-Action Learning

arXiv:2607. 04591v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated strong capabilities in robotic manipulation by integrating visual perception, language understanding, and robot action generation.

By Xinchuan Qiu, Yi Yu

robotics multimodal

Hugging Face Trending Papers

Jul 6

Simple-to-Complex Structured Demonstrations for Vision-Language-Action Learning

Vision-Language-Action (VLA) models have demonstrated strong capabilities in robotic manipulation by integrating visual perception, language understanding, and robot action generation. Existing research has primarily focused on improving model architectures, training strategies, and dataset scale, while little attention has been paid to how demonstrations are collected and organized.

robotics multimodal

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 Machine Learning

Jun 2

Coherent Off-Policy Improvement of Large Behavior Models with Learned Rewards

arXiv:2606. 02194v1 Announce Type: new Abstract: Distilling expert demonstration data into large generative models using behavioral cloning is a scalable approach to learning capable policies for robotic control, particularly for dexterous manipulation.

By Christian Scherer, Joe Watson, Theo Gruner, Daniel Palenicek, Ingmar Posner, Jan Peters

reinforcement-learning robotics fine-tuning

Related stories

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

Simple-to-Complex Structured Demonstrations for Vision-Language-Action Learning

Simple-to-Complex Structured Demonstrations for Vision-Language-Action Learning

Z-1: Efficient Reinforcement Learning for Vision-Language-Action Models

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

Coherent Off-Policy Improvement of Large Behavior Models with Learned Rewards