Mamba-based Selective State Space Modeling Improves the Accuracy-Complexity Tradeoff of SmolVLA Vision-Language-Action Experts
Vision-language-action (VLA) models face a crucial tradeoff between their task success rate and the policy-call frequency. Executing a single action per inference ($N=1$) enables accurate robot control but comes at the cost of huge compute time overheads, making real-time implementation infeasible. On the other hand, executing longer action horizons before replanning ($N\gg1$) reduces compute complexity, but inevitably degrades the system's success rate. In order to improve the VLA accuracy-complexity tradeoff, this paper investigates Mamba's selective state-space modeling as an alternative to causal self-attention within the action expert of the popular SmolVLA model, widely used as a re...
| Authors | Farida Mohsen, Thowayba Elkaffash, Mohammad Reza Chalak Qazani et al. |
|---|---|
| Primary category | cs.RO |
| Published | 10 August 2026 |
| Last updated at source | 10 August 2026 |
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