English(EN) LLaCO: Continuous Diffusion for Large Language Models with Competition-Aware Semantic Scheduling

LLaCO框架推进了大型语言模型的连续扩散

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-26 04:00

研究人员推出了一种新颖的大型语言模型框架LLaCO，该框架在嵌入空间中利用连续扩散。该方法通过引入竞争阈值理论（CTT）和语义保留调度（SRS）来解决时间压缩和令牌身份衰减问题。LLaCO采用嵌入方向适配器和时间步长条件调制，以及球形加噪过程，以改进去噪轨迹并保持准确的令牌竞争。LLaCO的8B模型实例已证明其性能优于现有的连续扩散模型，并可与同等规模的离散扩散模型相媲美。 AI

影响引入了一种新的基于扩散的大型语言模型方法，有望提高生成质量和效率。

排序理由该集群包含一篇详细介绍大型语言模型新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Zijian Jia, Peixuan Zhang, Genying Li, Di Wu, Yujie Fu, Si Li, Boxin Shi, Shuchen Weng · 2026-10-26 04:00

LLaCO：用于大型语言模型、具有竞争感知语义调度的连续扩散

arXiv:2610.32604v1 Announce Type: new Abstract: Continuous diffusion models have emerged as a promising non-autoregressive paradigm for text generation over continuous embedding spaces. Along the continuous trajectory, the residual token identity directly shapes the denoising obj…

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LLaCO：用于大型语言模型、具有竞争感知语义调度的连续扩散

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