---
license: other
pipeline_tag: robotics
tags:
- robotics
- vision-language-action models
---

# VLANeXt: Recipes for Building Strong VLA Models

[![arXiv](https://img.shields.io/badge/arXiv-2602.18532-b31b1b.svg)](https://huggingface.co/papers/2602.18532)
[![Project Page](https://img.shields.io/badge/Project-Page-green)](https://dravenalg.github.io/VLANeXt)
[![GitHub](https://img.shields.io/badge/GitHub-VLANeXt-black)](https://github.com/DravenALG/VLANeXt)
[![Awesome VLA](https://img.shields.io/badge/GitHub-AwesomeVLA-black)](https://github.com/DravenALG/awesome-vla)

VLANeXt is a Vision-Language-Action (VLA) model designed for general-purpose robotic policy learning. By systematically reexamining the VLA design space, the authors distill a set of 12 practical findings that significantly improve model performance and generalization across benchmarks like LIBERO and LIBERO-plus.

## 📖 Abstract

Following the rise of large foundation models, Vision–Language–Action models (VLAs) emerged, leveraging strong visual and language understanding for general-purpose policy learning. Yet, the current VLA landscape remains fragmented and exploratory. VLANeXt reexamines the VLA design space under a unified framework and evaluation setup, dissecting design choices along three dimensions: foundational components, perception essentials, and action modelling perspectives. The resulting model outperforms prior state-of-the-art methods and demonstrates strong generalization in real-world experiments.

## 🛠️ Usage

This repository hosts the checkpoints for evaluation on the LIBERO and LIBERO-plus benchmark suites. For environment setup, training, and evaluation instructions, please refer to the official [VLANeXt GitHub repository](https://github.com/DravenALG/VLANeXt).

## 📚 Citation

If you find VLANeXt useful for your research or applications, please cite the paper:

```bibtex
@article{wu2026vlanext,
    title={VLANeXt: Recipes for Building Strong VLA Models}, 
    author={Xiao-Ming Wu and Bin Fan and Kang Liao and Jian-Jian Jiang and Runze Yang and Yihang Luo and Zhonghua Wu and Wei-Shi Zheng and Chen Change Loy},
    journal={arXiv preprint arXiv:2602.18532},
    year={2026}
}
```

## 🗞️ License
This project is licensed under the [NTU S-Lab License 1.0](https://github.com/DravenALG/VLANeXt/blob/main/LICENSE).