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新方法通过推测性解码加速大语言模型推理速度

作者 PulseAugur 编辑部 · [2 个来源] ·

两篇新的研究论文介绍了加速大语言模型推理速度的新方法。第一篇论文“ReTrace”提出了一种技术，将每个草稿块条件化到上一轮被拒绝的后缀上，从而提高了 Qwen3 等模型的平均接受长度和解码速度。第二篇论文“Trajectory-Level Speculative Decoding”为基于扩散的语言模型（dLLMs）提出了一个框架，该框架对去噪轨迹进行推测，与 vanilla dLLMs 和 Fast-dLLM++ 等现有框架相比，实现了显著的加速。 AI

影响 推测性解码方面的这些进展可能导致大语言模型在各种应用中的部署更快、效率更高。

排序理由 两篇在 arXiv 上发表的学术论文，介绍了加速大语言模型推理的新方法。

在 arXiv cs.CL 阅读 →

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新方法通过推测性解码加速大语言模型推理速度

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两篇在 arXiv 上发表的学术论文，介绍了加速大语言模型推理的新方法。
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报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Luxi Lin, Zhanpeng Zeng, Shuang Peng, Songwei Liu, Rongrong Ji ·

    ReTrace：用于推测性解码的拒绝轨迹条件

    arXiv:2608.29748v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive language model inference by having a lightweight draft model propose multiple candidate tokens, which are then verified in parallel by a larger target model. However, after the first r…

  2. arXiv cs.CL TIER_1 English(EN) · Tianxiang Pan, Baitao Gong, Mo Guang, Hongwei Yong, Tianpeng Jiang, Yaqian Li, Zheng Cao, Kaiwen Long ·

    Trajectory-Level Speculative Decoding for Diffusion Language Models

    arXiv:2608.27514v1 Announce Type: new Abstract: Diffusion-based language models (dLLMs) enable parallel token generation through iterative denoising, but existing decoding strategies collapse to single-token generation under low confidence, severely limiting throughput. Unlike au…

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