StarVLA- alpha: Reducing Complexity in Vision-Language-Action Systems

arXiv:2604.11757 · cs.RO, cs.AI, cs.CV · Submitted 2026-04-13 · Read on arXiv

cs.RO, cs.AI, cs.CV

Submitted: 2026-04-13

Updated: 2026-09-16

Comments: Accepted by ECCV 2026

Code: https://github.com/starVLA/starVLA

Project page: https://starvla.github.io

License: http://creativecommons.org/licenses/by/4.0/

The gist: Vision-Language-Action (VLA) models have recently emerged as a promising paradigm for building general-purpose robotic agents.

Terminology

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 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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