Vision-Language-Action (VLA) models have shown promising results in multi-task robotic manipulation by leveraging visual and language understanding for action generation. However, existing lightweight VLA models employ a single dense action expert to handle all tasks, which limits their multi-task capability under constrained computational budgets. To address this issue, we propose SmolVLA-MoE, a novel framework that introduces sparse Mixture-of-Experts (MoE) into the FFN layers of the action expert in VLA models. SmolVLA-MoE replaces dense FFN layers with multiple parallel expert FFNs and a token-level router, enabling each token to adaptively select the most suitable expert for action generation. We further introduce a shared expert with a tunable weight parameter to balance the inheritance of pre-trained capabilities and task-specific expert specialization. Through sparse activation, SmolVLA-MoE activates only a single expert FFN during inference, with a total activated parameter count of approximately 500M including the frozen VLM backbone, while benefiting from approximately 800M total stored parameters. We evaluate our method on four task suites of the LIBERO benchmark. SmolVLA-MoE achieves an average success rate of 84.25%, outperforming SmolVLA (82.75%) and OpenVLA-7B (76.5%), while maintaining comparable inference efficiency. Our code is publicly available at https://github.com/dudu435/smolvla-moe.
Figures & tables
Figure 1: Overview of the SmolVLA-MoE architecture. The frozen VLM backbone processes visual observations and language instructions to produce contextual representations. The action expert replaces dense FFN layers with MoE-FFN layers, each containing a shared expert Es (always activated, weighted by α ), N routed expert FFNs, a token-level router with top- K selection, and a scaling adapter. Through sparse activation, only K experts are selected per token during both training and inference.
Figure 2: Detailed structure of the MoE-FFN layer. The input token is processed by both the shared expert and the top- K routed experts selected by the router. The scaling adapter provides additional per-token adaptive scaling. The final output is a weighted combination of the shared expert output (scaled by α ) and the routed expert output (scaled by 1−α ).
Figure 3: Visualization of complete rollouts in the LIBERO simulation environment. Each row shows key frames of the execution trajectory for a multi-step manipulation task from start to completion.
Method
Params
Spatial
Object
Goal
LIBERO-10
Avg.
Diffusion Policy [ 5 ]
321M
78.3
92.5
68.3
50.5
72.4
Octo [ 22 ]
93M
78.9
85.7
84.6
51.1
75.1
OpenVLA [ 14 ]
7B
84.7
88.4
79.2
53.7
76.5
π0 (Paligemma) [ 2 ]
3B
87
63
89
48
71.8
π0 [ 2 ]
3.3B
90
86
95
73
86.0
SmolVLA [ 25 ]
500M
87
93
88
63
82.75
Table 1: Main Results on the LIBERO Benchmark (Success Rate %)
Figure 4: Per-suite success rate comparison across all methods on the LIBERO benchmark. SmolVLA-MoE (highlighted with black borders) achieves competitive or superior performance across all four suites with ∼ 500M total activated parameters ( ∼ 100M in the action expert).
Figure 5: Heatmap visualization of the Top-K × shared expert weight ( α ) ablation study across all four LIBERO suites. Best values in each suite are shown in bold. Top-1 routing with α=0.3 achieves the best overall performance.
Configuration
Goal
SmolVLA-MoE (w/o shared expert, α=0 )
88
SmolVLA-MoE (w/ shared expert, α=0.3 )
93
Table 3: Ablation Study — Shared Expert (Success Rate %)
Configuration
Goal
SmolVLA-MoE (w/o load balancing, λ=0 )
90
SmolVLA-MoE (w/ load balancing, λ=0.01 )
93
Table 4: Ablation Study — Load Balancing Loss (Success Rate %)
Institute for Artificial Intelligence, Peking University, Beijing, China · School of Integrated Circuits, Peking University, Beijing, China · School of Electronics Engineering and Computer Science, Peking University, Beijing, China +2