SimVLA: A Simple VLA Baseline for Robotic Manipulation
Vision-Language-Action (VLA) models have emerged as a promising paradigm for general-purpose robotic manipulation, leveraging large-scale pre-training to achieve strong performance. The field has rapidly evolved with additional spatial priors and diverse architectural innovations. However, these advancements are often accompanied by varying training recipes and implementation details, which can make it challenging to disentangle the precise source of empirical gains. In this work, we introduce SimVLA, a streamlined baseline designed to establish a transparent reference point for VLA research. By strictly decoupling perception from control, using a standard vision-language backbone and a lightweight action head, and standardizing critical training dynamics, we demonstrate that a minimal design can achieve state-of-the-art performance. Despite having only 0.5B parameters, SimVLA outperforms multi-billion-parameter models on standard simulation benchmarks without robot pretraining. SimVLA also reaches on-par real-robot performance compared to pi0.5. Our results establish SimVLA as a robust, reproducible baseline that enables clear attribution of empirical gains to future architectural innovations. Website: https://frontierrobo.github.io/SimVLA
699d2f004e37ec6dfa1bc67e
Yuankai Luo
claimed_verified
2026-02-24T09:49:51.304Z
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/avatars/69a93e4455b46bbd821cf977ca859a9f.svg
Yuankai Luo
YuankaiLuo
user
YuankaiLuo
699d2f004e37ec6dfa1bc67f
Woping Chen
699d2f004e37ec6dfa1bc680
Tong Liang
699d2f004e37ec6dfa1bc681
Baiqiao Wang
699d2f004e37ec6dfa1bc682
Zhenguo Li
SimVLA: A Simple VLA Baseline for Robotic Manipulation
Vision-Language-Action (VLA) models have emerged as a promising paradigm for general-purpose robotic manipulation, leveraging large-scale pre-training to achieve strong performance. The field has rapidly evolved with additional spatial priors and diverse architectural innovations. However, these advancements are often accompanied by varying training recipes and implementation details, which can make it challenging to disentangle the precise source of empirical gains. In this work, we introduce SimVLA, a streamlined baseline designed to establish a transparent reference point for VLA research. By strictly decoupling perception from control, using a standard vision-language backbone and a lightweight action head, and standardizing critical training dynamics, we demonstrate that a minimal design can achieve state-of-the-art performance. Despite having only 0.5B parameters, SimVLA outperforms multi-billion-parameter models on standard simulation benchmarks without robot pretraining. SimVLA also reaches on-par real-robot performance compared to pi0.5. Our results establish SimVLA as a robust, reproducible baseline that enables clear attribution of empirical gains to future architectural innovations. Website: https://frontierrobo.github.io/SimVLA
SimVLA presents a simplified baseline for Vision-Language-Action models that achieves state-of-the-art performance with fewer parameters while enabling clearer evaluation of architectural improvements.
Vision-Language-Action models perception control vision-language backbone action head training dynamics parameter-efficient fine-tuning simulation benchmarks real-robot performance