ProbeFlow: Training-Free Adaptive Flow Matching for Vision-Language-Action Models

• URL: http://arxiv.org/abs/2603.17850v1
• Date: Wed, 18 Mar 2026 15:38:29 GMT
• Title: ProbeFlow: Training-Free Adaptive Flow Matching for Vision-Language-Action Models
• Abstract summary: Recent Vision-Language-Action (VLA) models equipped with Flow Matching (FM) action heads achieve state-of-the-art performance in complex robot manipulation.<n>While current acceleration efforts optimize the Vision-Language Model (VLM) backbone, the action head bottleneck remains overlooked.<n>We propose ProbeFlow, a training-free adaptive inference framework for continuous robotic control.
• Score: 12.232846303286161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent Vision-Language-Action (VLA) models equipped with Flow Matching (FM) action heads achieve state-of-the-art performance in complex robot manipulation. However, the multi-step iterative ODE solving required by FM introduces inference latency that precludes responsive physical control. While current acceleration efforts optimize the Vision-Language Model (VLM) backbone, the action head bottleneck remains overlooked. To address this, we propose ProbeFlow, a training-free adaptive inference framework tai- lored for continuous robotic control. By evaluating geometric trajectory complexity via the cosine similarity between initial and lookahead velocity vectors, ProbeFlow dynamically sched- ules integration steps to prune redundant network evaluations. On the MetaWorld benchmark, it accelerates action decoding by 14.8x (reducing average steps from N = 50 to 2.6) and cuts end-to-end system latency by 2.8x without compromising the manipulation success rate. On the long-horizon LIBERO benchmark, the probe automatically allocates a denser schedule to navigate semantic bottlenecks, effectively resolving the flow solver delay. Real-world physical deployments confirm that ProbeFlow successfully mitigates action decoding latency while ensuring execution stability, offering a highly practical solution for low-latency continuous generative policies.

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