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papersTODAY 04:00 UTC

Study Compares SmolVLA Task Success and Latency Across PyTorch and ONNX Deployments

A new arXiv paper examines how deploying the SmolVLA vision-language-action model in different runtime formats affects both inference speed and closed-loop task performance. The authors benchmark HuggingFaceVLA/smolvla_libero on a 6 GB RTX 2060 across the LIBERO Spatial and Object suites using MuJoCo and LeRobot with a fixed seed. The results indicate that cutting latency through optimized deployment can shift task behavior, so faster inference does not automatically mean better outcomes.

Hugging Face LIBERO ONNX PyTorch SmolVLA vision-language-action

COVERAGE · 1 REPORT · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.LGWhen Faster VLA Deployment Changes Closed-Loop Behavior: Task Success-Latency Analysis of SmolVLA Across PyTorch and ONNX Variants ↗TODAY 04:00 UTC