← Back to all news
arXiv Computer Vision By Shiyang Zhang, Miao Yang, Zhengda Li, Yang Shao, Ruifang Zhai, Hui Peng, Peng Song

SmolVLA-MoE: Sparse Mixture-of-Experts for Efficient Multi-Task Robotic Manipulation

Read the original on arXiv Computer Vision →

SmolVLA-MoE introduces a sparse Mixture-of-Experts (MoE) architecture into Vision‑Language‑Action (VLA) models for robotic manipulation, replacing dense feed‑forward networks with multiple expert FFNs and a token‑level router. The framework includes a shared expert with a tunable weight to balance pre‑trained knowledge and task‑specific specialization, activating only one expert during inference to keep the parameter count low. Evaluated on the LIBERO benchmark, SmolVLA‑MoE achieves an average success rate of 84.25%, surpassing both SmolVLA and OpenVLA‑7B while maintaining comparable inference efficiency.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv AI
Jun 19

Finetuning Vision-Language-Action Models Requires Fewer Layers Than You Think

arXiv:2606. 20246v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models pre-trained on massive video-robot datasets have revolutionized robotic manipulation, yet their multi-billion parameter architectures impose prohibitive computational burdens during downstream fine-tuning and real-time inference.

By Gia-Binh Nguyen, Trong-Bao Ho, Thien-Loc Ha, Khoa Vo, Philip Lund M{\o}ller, Quang T. Nguyen, Long Dinh, Tuan Dam, Vu Duong, Tung M. Luu, Trung Le, Tran Nguyen Le, Minh Vu, An Thai Le, Ngan Le, Daniel Sonntag, James Zou, Jan Peters, Duy M. H. Nguyen, Ngo Anh Vien
roboticsfine-tuningmultimodalbenchmarkssafety
More like this →
arXiv AI
Jul 9

VOTE: Vision-Language-Action Optimization with Trajectory Ensemble Voting

arXiv:2507. 05116v5 Announce Type: replace-cross Abstract: Recent large-scale Vision Language Action (VLA) models have shown superior performance in robotic manipulation tasks guided by natural language.

By Juyi Lin, Amir Taherin, Arash Akbari, Arman Akbari, Lei Lu, Guangyu Chen, Taskin Padir, Xiaomeng Yang, Weiwei Chen, Yiqian Li, Xue Lin, David Kaeli, Pu Zhao, Yanzhi Wang
roboticsefficiencymultimodalbenchmarks
More like this →
arXiv Computer Vision
Aug 31

Beyond Data Scaling: Representation-Centric Continued Pre-training for Vision-Language-Action Models

The paper introduces VLAct, a Vision‑Language‑Action model that focuses on representation‑centric continued pre‑training rather than merely scaling robot data. VLAct is trained on diverse, multi‑embodiment robot data and preserves a broad VLM prior while encouraging shared action semantics across embodiments. Experiments across simulation, real‑world, and unseen‑embodiment settings show that VLAct consistently outperforms existing industrial VLA systems, achieving high success rates with only a modest compute budget and open‑source data.

By Senqiao Yang, Chengyao Wang, Yuxin Chen, Zixuan Wang, Longxiang Tang, Haokun Gui, Jinhui Ye, Changsheng Lu, Xiaoyang Wu, Mingkang Zhu, Pengguang Chen, Shu Liu, Zhuotao Tian, Hengshuang Zhao, Bei Yu, Jiaya Jia
roboticsfine-tuningmultimodal
More like this →
arXiv AI
Sep 18

StarVLA-$\alpha$: Reducing Complexity in Vision-Language-Action Systems

StarVLA-α is a streamlined Vision‑Language‑Action (VLA) model that reduces architectural and pipeline complexity to facilitate systematic study of VLA design choices. By employing a strong VLM backbone and minimal design, it achieves competitive performance across multiple benchmarks (LIBERO, SimplerEnv, RoboTwin, RoboCasa) and outperforms the baseline π₀.₅ by 20% on the RoboChallenge benchmark. The authors plan to release the code to support future VLA research.

By Jinhui Ye, Ning Gao, Senqiao Yang, Jinliang Zheng, Zixuan Wang, Yuxin Chen, Pengguang Chen, Yilun Chen, Shu Liu, Jiaya Jia
agentsroboticsmultimodalbenchmarks
More like this →