SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics

Abstract

Vision-language models (VLMs) pretrained on large-scale multimodal datasets encode rich visual and linguistic knowledge, making them a strong foundation for robotics. Rather than training robotic policies from scratch, recent approaches adapt VLMs into vision-language-action (VLA) models that enable natural language-driven perception and control. However, existing VLAs are typically massive--often with billions of parameters--leading to high training costs and limited real-world deployability. Moreover, they rely on academic and industrial datasets, overlooking the growing availability of community-collected data from affordable robotic platforms. In this work, we present SmolVLA, a small, efficient, and community-driven VLA that drastically reduces both training and inference costs, while retaining competitive performance. SmolVLA is designed to be trained on a single GPU and deployed on consumer-grade GPUs or even CPUs. To further improve responsiveness, we introduce an asynchronous inference stack decoupling perception and action prediction from action execution, allowing higher control rates with chunked action generation. Despite its compact size, SmolVLA achieves performance comparable to VLAs that are 10x larger. We evaluate SmolVLA on a range of both simulated as well as real-world robotic benchmarks and release all code, pretrained models, and training data.

Full paper on arXiv · Code

Details

arXiv identifier

2506.01844

Published

2025-06-02

Authors

Mustafa Shukor, Dana Aubakirova, Francesco Capuano, Pepijn Kooijmans, Steven Palma, Adil Zouitine, Michel Aractingi, Caroline Pascal, Martino Russi, Andres Marafioti, Simon Alibert, Matthieu Cord, Thomas Wolf, Remi Cadene

Models That Cite This Paper

• Described bysmolvla_base
• Described bysmolvla_libero
• Described bysmolvla_libero
• Described bysmolvla_robotwin
• Described bysmolvla-policy-test
• Described bysmolVLA_cube_training_EE_1.1
• Described bySmolVLA-Base-0.45B
• Described bysmolvla_policy_so101_multitask_pnp_stack_onehot_0917
• Described bysmolvla_omx_ai_test
• Described byfranka-stack-150e-256-smolvla-continued