Mamba-based Selective State Space Modeling Improves the Accuracy-Complexity Tradeoff of SmolVLA Vision-Language-Action Experts
Researchers have proposed a new approach to improve the performance of vision-language-action models in robotics. They used Mamba's selective state-space modeling instead of causal self-attention within the action expert of the SmolVLA model. The results show that the Mamba-based expert outperforms the Transformer baseline by 7.8% and 3.7% when executing 50 and 25 actions before replanning, respectively. Additionally, the Mamba variant matches the Transformer's mean success r
Researchers have proposed a new approach to improve the performance of vision-language-action models in robotics. They used Mamba's selective state-space modeling instead of causal self-attention within the action expert of the SmolVLA model. The results show that the Mamba-based expert outperforms the Transformer baseline by 7.8% and 3.7% when executing 50 and 25 actions before replanning, respectively. Additionally, the Mamba variant matches the Transformer's mean success rate while reducing overall model parameter complexity by 24%. The study was published on arXiv.
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Why it matters: This research is important for engineers working on robotics and AI as it provides a new approach to improve the accuracy-complexity tradeoff of vision-language-action models. This can lead to more efficient and effective robot control systems.
Source: https://arxiv.org/abs/2608.21407
This article was originally published at: https://arxiv.org/abs/2608.21407