LoRA: Low-Rank Adaptation of Large Language Models
Low-rank trainable updates adapt a frozen pretrained model without updating all of its parameters.
THE RESEARCH ATLAS
SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics
A compact vision-language-action model explores accessible robot learning and an efficient inference pipeline.
Institution and location sources
Location refers to credited institutions or the listed organization, not author citizenship or an exhaustive collaboration list.
Why read this
Read alongside larger VLA models to understand the role of data and system design.
Citation
@misc{shukor2025smolvla,
title = {SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics},
author = {Mustafa Shukor and Dana Aubakirova and Francesco Capuano and Pepijn Kooijmans and Steven Palma and Adil Zouitine and Michel Aractingi and Caroline Pascal and Martino Russi and Andres Marafioti and Simon Alibert and Matthieu Cord and Thomas Wolf and Remi Cadene},
year = {2025},
eprint = {2506.01844},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2506.01844}
}Rights remain with the original authors. Refer to the original source for citations and licensing.
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