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

TL;DR

StarVLA-α introduces a simplified, strong baseline for vision-language-action models that achieves competitive performance across multiple benchmarks, emphasizing minimal design complexity.

Contribution

It presents a minimalistic yet effective VLA baseline that simplifies architecture and training, enabling systematic analysis and strong performance.

Findings

The baseline remains highly competitive across multiple benchmarks.

A single generalist model outperforms previous methods on RoboChallenge.

Minimal design can achieve strong results without complex engineering.

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-$\alpha$, a simple yet strong baseline designed to study VLA design choices under controlled conditions. StarVLA-$\alpha$ 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 $\pi_{0.5}$ by 20\% on the public real-world RoboChallenge benchmark. We expect StarVLA-$\alpha$ to serve as a solid starting point for future research in the VLA regime. Code will be released at https://github.com/starVLA/starVLA.