StarVLA-$α$: Reducing Complexity in Vision-Language-Action Systems

• URL: http://arxiv.org/abs/2604.11757v1
• Date: Mon, 13 Apr 2026 17:30:01 GMT
• Title: StarVLA-$α$: Reducing Complexity in Vision-Language-Action Systems
• Abstract summary: StarVLA-$$ is a simple yet strong baseline to study VLA design choices under controlled conditions.<n>We re-evaluate several key design axes, including action modeling strategies, robot-specific pretraining, and interface engineering.<n>Our single generalist model outperforms $_0.5$ by 20% on the public real-world RoboChallenge benchmark.
• Score: 89.12403322299963
• License: http://creativecommons.org/licenses/by/4.0/
• 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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