StarVLA-$α$: Reducing Complexity in Vision-Language-Action Systems
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,...
| Authors | Jinhui Ye, Ning Gao, Senqiao Yang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 13 April 2026 |
| Last updated at source | 16 September 2026 |
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