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New STAND technique slashes LLM reasoning latency by 65%

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了 STAND（STochastic Adaptive N-gram Drafting），一种新的无模型推测解码技术，旨在加速语言模型推理。该方法利用推理轨迹中的冗余来更有效地预测 token，而无需单独的草稿模型。STAND 在各种推理任务和模型上已证明可将推理延迟减少 60-65%，同时保持准确性并优于现有的推测解码方法。 AI

影响 加速 LLM 推理速度，可能实现更复杂的推理任务和更广泛的部署。

排序理由 发表了一篇详细介绍加速语言模型推理新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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New STAND technique slashes LLM reasoning latency by 65%

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发表了一篇详细介绍加速语言模型推理新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Woomin Song, Saket Dingliwal, Sai Muralidhar Jayanthi, Bhavana Ganesh, Jinwoo Shin, Aram Galstyan, Sravan Babu Bodapati ·

    Accelerated Test-Time Scaling with Model-Free Speculative Sampling

    arXiv:2506.04708v3 Announce Type: replace Abstract: Language models have demonstrated remarkable capabilities in reasoning tasks through test-time scaling techniques like best-of-N sampling and tree search. However, these approaches often demand substantial computational resource…

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