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English(EN) When Faster VLA Deployment Changes Closed-Loop Behavior: Task Success-Latency Analysis of SmolVLA Across PyTorch and ONNX Variants

新研究探讨VLA模型效率和延迟权衡 · 跟踪2个来源

作者 PulseAugur 编辑部 · [2 个来源] ·

两篇新研究论文探讨了视觉-语言-动作（VLA）模型的效率和性能。第一篇论文分析了SmolVLA，展示了ONNX等部署优化如何显著降低延迟，但可能会影响任务成功率，尤其是在空间任务中。第二篇论文研究了高效VLA模型的设计，发现动作头初始化对性能至关重要，并且当前的扩展实践在准确性方面可能会带来边际效益递减的延迟。 AI

影响 这些研究突出了VLA模型设计和部署的关键因素，可能指导未来朝着更高效、更有效的系统发展。

排序理由 两篇在arXiv上发表的学术论文，详细介绍了对VLA模型性能和效率的研究。

在 arXiv cs.LG 阅读 →

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新研究探讨VLA模型效率和延迟权衡 · 跟踪2个来源

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两篇在arXiv上发表的学术论文，详细介绍了对VLA模型性能和效率的研究。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Rafiqul Islam ·

    更快的VLA部署如何改变闭环行为：PyTorch和ONNX变体上SmolVLA的任务成功率-延迟分析

    arXiv:2609.14146v1 Announce Type: cross Abstract: Vision-language-action (VLA) deployment can reduce inference latency while changing closed-loop task behavior. We evaluate HuggingFaceVLA/smolvla_libero on an RTX 2060 (6 GB) in LIBERO Spatial and Object (MuJoCo 3.3.2, LeRobot 0.6…

  2. arXiv cs.CV TIER_1 English(EN) · Luoyang Sun, Guoyang Xia, Fengfa Li, Lei Ren, Xinyu Cui, Haifeng Zhang, Fangxiang Feng, Kaike Zhang, Kun Zhan, Yan Xie, Jun Wang, Cheng Deng ·

    高效VLA的要素是什么？探索动作头设计、扩展性和延迟

    arXiv:2609.13984v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models combine a pretrained vision encoder, a language backbone, and an action head, but their relative contribution has not been established under controlled, latency-paired conditions. We fix the bac…

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