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新研究探索用于加速大语言模型推理的投机解码

两篇新研究论文探讨了加速大语言模型(LLM)推理的方法。第一篇论文《Carryover Drafting》介绍了一种在投机解码过程中重复使用被拒绝标记的隐藏状态的技术,从而提高了接受长度和整体加速效果。第二篇论文《How Lossless Is Lossless Speculative Decoding?》质疑了“Orthrus”架构的精确轨迹匹配声明,发现数值精度显著影响投机输出是否与自回归模型的输出完全匹配,尽管下游性能基本不受影响。 AI

影响 这些论文提出了改进大语言模型推理速度的方法,并分析了投机解码技术的保真度,可能导致更高效的AI部署。

排序理由 两篇在arXiv上发表的学术论文,详细介绍了LLM推理加速的新技术和分析。

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新研究探索用于加速大语言模型推理的投机解码

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两篇在arXiv上发表的学术论文,详细介绍了LLM推理加速的新技术和分析。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Jahyun Koo, Sunghyeon Woo, Jaeeun Kil, Jeongtae Lee, Sungjae Lee, Kyomin Jung, Minsub Kim ·

    遗留草稿:利用被拒绝的状态进行推测性解码

    arXiv:2609.14717v1 Announce Type: cross Abstract: Speculative decoding accelerates LLM inference by verifying multiple drafted tokens in parallel, allowing a single target forward pass to accept several tokens. By construction, verification computes representations for both accep…

  2. arXiv cs.AI TIER_1 English(EN) · Ilya Koziev, Leonid Sinev, Ivan Oseledets ·

    无损投机解码有多无损?数值精度在Orthrus中的作用

    arXiv:2609.15504v1 Announce Type: cross Abstract: Orthrus is a hybrid autoregressive-diffusion architecture that accelerates autoregressive language-model inference by generating multiple tokens in parallel while using a frozen autoregressive backbone. Its central claim is that a…