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English(EN) AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding

新研究提升LLM投机解码速度和效率 · 跟踪4个来源

作者 PulseAugur 编辑部 · [5 个来源] ·

arXiv上发表的四篇新研究论文介绍了增强大型语言模型投机解码的新技术。这些方法旨在提高生成速度和效率，而无需额外的模型训练。技术包括使用验证器计算的语义键、近似最长前缀选择、MoE模型的承诺加权专家集以及半自回归模型的父节点条件草稿树。这些论文共同展示了在包括Qwen3和DeepSeek-V4在内的各种基准和模型尺寸上的显著加速和改进的接受率。 AI

影响 这些技术可以显著加速LLM推理，使实时应用更可行并降低计算成本。

排序理由 多篇arXiv论文介绍了用于LLM推理优化的新研究方法。

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 5 个来源。 我们如何撰写摘要 →

新研究提升LLM投机解码速度和效率 · 跟踪4个来源

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多篇arXiv论文介绍了用于LLM推理优化的新研究方法。
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报道来源 [5]

  1. arXiv cs.CL TIER_1 English(EN) · Amirmohammad Karimi, Chao Gao, Negar Hassanpour ·

    DBLAST：用于随机推测解码的依赖块草稿

    arXiv:2608.05448v1 Announce Type: new Abstract: Speculative decoding accelerates large language models' inference by using a lightweight drafter to propose multiple future tokens and a target model to verify them. While recent block and diffusion-style drafters can predict severa…

  2. arXiv cs.AI TIER_1 English(EN) · Tao Jin, Phuong Minh Nguyen, Zhenzhu Yan, Teeradaj Racharak, Naoya Inoue ·

    Oilbird：无需训练的推测解码，利用验证器已计算的键

    arXiv:2608.03839v1 Announce Type: new Abstract: Training-free speculative decoding drafts by matching an exact suffix of the context against a pool of earlier context. That lookup misses correct drafts already in the pool, most visibly on tool-calling traffic, where a request rep…

  3. arXiv cs.AI TIER_1 Română(RO) · Yuannuo Feng, Zegang Peng, Yuxin Xie, Yubing Ye, Yizhe Chen, Wenshuai Yao, Wenyong Zhou, Wang Kang ·

    近似推测解码

    arXiv:2608.03447v1 Announce Type: cross Abstract: Speculative decoding accelerates autoregressive generation by verifying a draft block with a target model in parallel. Under standard greedy verification, decoding stops at the first draft token that differs from the target argmax…

  4. arXiv cs.CL TIER_1 English(EN) · Shuang Liang (Mark), Hao (Mark), Chen, Zhiwen Mo, Qianzhou Wang, Guoyu Li, Lingxiao Ma, Wayne Luk ·

    AcceptMoE：承诺加权自适应验证专家集，实现高效MoE投机解码

    arXiv:2608.02989v1 Announce Type: cross Abstract: Speculative decoding verifies a tree of draft tokens in one target-model forward pass. For a mixture-of-experts (MoE) target, however, parallel verification can activate the union of the experts selected by all tree nodes, even th…

  5. arXiv cs.CL TIER_1 English(EN) · Zixian Li, Tong Li, Chi Xie, Xiaohui Song, Haonan Lu ·

    从链到树：用于半自回归推测性解码的父代条件草稿

    arXiv:2608.02123v1 Announce Type: new Abstract: Speculative decoding accelerates LLM inference only when drafted continuations survive target-model verification. Semi-autoregressive drafters such as DSpark predict an entire token block with one backbone forward and refine it with…

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