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[2602.18532] VLANeXt: Recipes for Building Strong VLA Models
Summary
The paper presents VLANeXt, a framework for building effective Vision-Language-Action (VLA) models, addressing inconsistencies in training and evaluation protocols within the field.
Why It Matters
As the landscape of VLA models evolves, establishing a unified framework is crucial for researchers and developers. VLANeXt provides practical guidelines and a codebase to enhance model performance and facilitate reproducibility in research, ultimately advancing the field of AI.
Key Takeaways
- VLANeXt offers a structured approach to VLA model development.
- The study identifies 12 key findings that inform effective design choices.
- VLANeXt outperforms existing state-of-the-art models on key benchmarks.
- A unified codebase will be released to support community collaboration.
- The framework enhances generalization capabilities in real-world applications.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.18532 (cs) [Submitted on 20 Feb 2026] Title:VLANeXt: Recipes for Building Strong VLA Models Authors:Xiao-Ming Wu, Bin Fan, Kang Liao, Jian-Jian Jiang, Runze Yang, Yihang Luo, Zhonghua Wu, Wei-Shi Zheng, Chen Change Loy View a PDF of the paper titled VLANeXt: Recipes for Building Strong VLA Models, by Xiao-Ming Wu and 8 other authors View PDF HTML (experimental) 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. Although many groups have proposed their own VLA models, inconsistencies in training protocols and evaluation settings make it difficult to identify which design choices truly matter. To bring structure to this evolving space, we reexamine the VLA design space under a unified framework and evaluation setup. Starting from a simple VLA baseline similar to RT-2 and OpenVLA, we systematically dissect design choices along three dimensions: foundational components, perception essentials, and action modelling perspectives. From this study, we distill 12 key findings that together form a practical recipe for building strong VLA models. The outcome of this exploration is a simple yet effective model, VLANeXt. VLANeXt outperforms prior state-of-the-art methods on the LIBERO and LIBERO-plus benchmarks and...
