Less is More: Reducing Complexity in Vision-Language-Action Systems

Vision-Language-Action (VLA) models have emerged as apromising paradigm for building general-purpose robotic agents. How-ever, the VLA landscape remains highly fragmented and complex: asexisting approaches vary substantially in architectures, training data,embodiment configurations, and benchmark-specific engineering. In thiswork, we introduce StarVLA-α, a simple yet strong baseline designedto study VLA design choices under controlled conditions. StarVLA-αdeliberately minimizes architectural and pipeline complexity to reduceexperimental confounders and enable systematic analysis. Specifically,we re-evaluate several key design axes, including action modeling strate-gies, robot-specific pretraining, and interface engineering. Across uni-fied multi-benchmark training on LIBERO, SimplerEnv, RoboTwin, andRoboCasa, the same simple baseline remains highly competitive, indi-cating that a strong VLM backbone combined with minimal design isalready sufficient to achieve strong performance without relying on ad-ditional architectural complexity or engineering tricks. Notably, our sin-gle generalist model outperforms π0.5 by 20% on the public real-worldRoboChallenge benchmark. We expect StarVLA-α to serve as a solidstarting point for future research in the VLA regime. Code will be re-leased at https://github.com/starVLA/starVLA.