VLANeXt: Recipes for Building Strong VLA Models
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 demonstrates strong generalization in real-world experiments. We will release a unified, easy-to-use codebase that serves as a common platform for the community to reproduce our findings, explore the design space, and build new VLA variants on top of a shared foundation.
699d29bf4e37ec6dfa1bc66d
Xiao-Ming Wu
claimed_verified
2026-02-24T09:50:14.717Z
61dd2b7389dddd97daead12f
https://cdn-avatars.huggingface.co/v1/production/uploads/61dd2b7389dddd97daead12f/xUL7YK3Mtvzz4rEhorFKF.jpeg
Xiao-Ming Wu
DravenALG
user
DravenALG
699d29bf4e37ec6dfa1bc66e
Bin Fan
699d29bf4e37ec6dfa1bc66f
Kang Liao
699d29bf4e37ec6dfa1bc670
Jian-Jian Jiang
699d29bf4e37ec6dfa1bc671
Runze Yang
699d29bf4e37ec6dfa1bc672
Yihang Luo
699d29bf4e37ec6dfa1bc673
Zhonghua Wu
699d29bf4e37ec6dfa1bc674
Wei-Shi Zheng
699d29bf4e37ec6dfa1bc675
Chen Change Loy
VLANeXt: Recipes for Building Strong VLA Models
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 demonstrates strong generalization in real-world experiments. We will release a unified, easy-to-use codebase that serves as a common platform for the community to reproduce our findings, explore the design space, and build new VLA variants on top of a shared foundation.
Vision-Language-Action models are systematically analyzed and optimized through a unified framework, resulting in the VLANeXt model that achieves superior performance on benchmark tasks and demonstrates strong real-world generalization.
Vision-Language-Action models foundation models policy learning VLA design space RT-2 OpenVLA perception essentials action modelling perspectives LIBERO LIBERO-plus VLANeXt