Paper Detail
Jinhui Ye, Ning Gao, Senqiao Yang, Jinliang Zheng, Zixuan Wang, Yuxin Chen, Pengguang Chen, Yilun Chen, Shu Liu, Jiaya Jia
Vision-Language-Action (VLA) models have recently emerged as a promising paradigm for building general-purpose robotic agents. However, the VLA landscape remains highly fragmented and complex: as existing approaches vary substantially in architectures, training data, embodiment configurations, and benchmark-specific engineering. In this work, we introduce StarVLA-$α$, a simple yet strong baseline designed to study VLA design choices under controlled conditions. StarVLA-$α$ deliberately minimizes architectural and pipeline complexity to reduce experimental confounders and enable systematic analysis. Specifically, we re-evaluate several key design axes, including action modeling strategies, robot-specific pretraining, and interface engineering. Across unified multi-benchmark training on LIBERO, SimplerEnv, RoboTwin, and RoboCasa, the same simple baseline remains highly competitive, indicating that a strong VLM backbone combined with minimal design is already sufficient to achieve strong performance without relying on additional architectural complexity or engineering tricks. Notably, our single generalist model outperforms $π_{0.5}$ by 20\% on the public real-world RoboChallenge benchmark. We expect StarVLA-$α$ to serve as a solid starting point for future research in the VLA regime. Code will be released at https://github.com/starVLA/starVLA.
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@article{ye2026starvla,
title = {StarVLA-\$α\$: Reducing Complexity in Vision-Language-Action Systems},
author = {Jinhui Ye and Ning Gao and Senqiao Yang and Jinliang Zheng and Zixuan Wang and Yuxin Chen and Pengguang Chen and Yilun Chen and Shu Liu and Jiaya Jia},
year = {2026},
abstract = {Vision-Language-Action (VLA) models have recently emerged as a promising paradigm for building general-purpose robotic agents. However, the VLA landscape remains highly fragmented and complex: as existing approaches vary substantially in architectures, training data, embodiment configurations, and benchmark-specific engineering. In this work, we introduce StarVLA-\$α\$, a simple yet strong baseline designed to study VLA design choices under controlled conditions. StarVLA-\$α\$ deliberately minimizes},
url = {https://arxiv.org/abs/2604.11757},
keywords = {cs.RO, cs.AI, cs.CV},
eprint = {2604.11757},
archiveprefix = {arXiv},
}
{}