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English(EN) DeLS-Spec: Decoupled Long-Short Contexts for Parallel Speculative Drafting

新的DeLS-Spec方法通过解耦上下文加速LLM推理

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出了一种名为DeLS-Spec的新方法，通过解耦长短上下文推测解码来加速大型语言模型推理。该方法使用一个固定的长上下文专家DFlash和一个轻量级的、可独立训练的短上下文专家。与需要从头开始训练的Domino和DSpark等先前方法相比，DeLS-Spec提供了显著更低的训练成本和更高的模块化。在Qwen3模型上的实验表明，DeLS-Spec在各种基准测试中提高了加速效果和平均接受长度。 AI

影响 该方法可能带来更快、更高效的LLM推理，降低计算成本并改善用户体验。

排序理由 这是一篇详细介绍LLM推理加速新方法的学术论文。

在 arXiv cs.CL 阅读 →

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新的DeLS-Spec方法通过解耦上下文加速LLM推理

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报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Hong-Kai Zheng, Piji Li ·

    DeLS-Spec：用于并行推测性草稿的解耦长短上下文

    arXiv:2607.07409v1 Announce Type: new Abstract: Speculative decoding accelerates LLM inference by drafting multiple tokens and verifying them in parallel. Block-parallel drafters such as DFlash further improve drafting efficiency by predicting an entire block in one pass, but the…

  2. arXiv cs.CL TIER_1 English(EN) · Piji Li ·

    DeLS-Spec：用于并行推测性草稿的解耦长短上下文

    Speculative decoding accelerates LLM inference by drafting multiple tokens and verifying them in parallel. Block-parallel drafters such as DFlash further improve drafting efficiency by predicting an entire block in one pass, but their position-wise predictions lack explicit intra…

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