title: SmolVLA: A Vision-Language-Action Model for Affordable and Efficient
    Robotics
summary: 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.
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paper_title: SmolVLA: A Vision-Language-Action Model for Affordable and Efficient
    Robotics
paper_summary: 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.
paper_ai_summary: SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
paper_ai_keywords: vision-language models multimodal datasets robotic policies vision-language-action models natural language-driven perception asynchronous inference action prediction action execution chunked action generation performance benchmarks