FlowBlock 框架通过并行处理加速扩散式大语言模型解码

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-20 08:05

研究人员开发了 FlowBlock，一个新颖的无需训练的框架，旨在加速扩散式大语言模型 (dLLMs) 的解码过程。该方法通过允许块并行处理信息，而不是严格按顺序处理，引入了并行解码。FlowBlock 实现了显著的加速，比 LLaDA-2.0 等现有模型快 4.01 倍，同时还提高了准确性并降低了延迟。 AI

影响这个新框架可能会显著加快基于扩散式的大语言模型的推理速度，从而可能降低成本并实现新的实时应用。

排序理由该集群描述了一篇新的研究论文，其中详细介绍了一个新颖的框架，用于提高大语言模型的解码效率。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

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Newsworthiness bucket: Tool; 该集群描述了一篇新的研究论文，其中详细介绍了一个新颖的框架，用于提高大语言模型的解码效率。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

Hugging Face Daily Papers TIER_1 English(EN) · 2026-07-20 08:05

FlowBlock：面向自纠正扩散语言模型的波前并行解码

Block-wise diffusion large language models (dLLMs) decode sequentially at the block level, enabling effective KV-cache reuse across blocks but making inter-block decoding strictly serial. Prior work has attempted to unlock inter-block parallelism through post-training methods, bu…

本文如何被排名

报道来源 [1]

FlowBlock：面向自纠正扩散语言模型的波前并行解码

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