English(EN) HaloBlock: Faster and Better Decoding for Diffusion Large Language Models

HaloBlock 提高了 dLLM 的解码速度和准确性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员推出了一种名为 HaloBlock 的新机制，旨在提高扩散大语言模型 (dLLM) 解码的效率和质量。该方法解决了解码难度集中在块的右侧以及块之间上下文不连续等限制。通过使用双向累积熵评估块解码难度，HaloBlock 自适应地分配光环区域以平滑上下文传播并降低挑战性 token 的难度。在 LLaDA-8B-Instruct、Dream-7B-Instruct 和 LLaDA-1.5 等模型上的评估显示，在数学推理和代码生成等任务中的准确性和推理速度均有所提高。 AI

影响 HaloBlock 为扩散大语言模型提供了更快、更准确的推理途径，尤其是在编码和推理等复杂任务中。

排序理由该集群包含一篇研究论文，详细介绍了一种改进 LLM 解码的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Zhaohui Zheng, Chenhang He, Jinrui Zhang, Ming-Ming Cheng, Lei Zhang · 2026-10-15 04:00

HaloBlock：更快更好的扩散大型语言模型解码

arXiv:2610.17948v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) have attracted increasing research attention due to their bidirectional attention and arbitrary order decoding. However, there are two issues in dLLMs inference: the decoding difficulty is d…

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HaloBlock：更快更好的扩散大型语言模型解码

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