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PRISM模型在序列建模中实现174倍吞吐量

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了PRISM，这是一种新颖的序列建模架构，旨在平衡Transformer的表达能力和线性模型的效率。PRISM通过将迭代过程重构为可并行化的形式，解决了Test-Time Training等迭代方法中存在的串行依赖问题。这通过写入-遗忘解耦策略和两阶段代理架构实现，与现有的优化方法相比，吞吐量显著提高。 AI

影响 引入了一种新的可并行化架构，显著提高了序列建模任务的吞吐量。

排序理由 该集群包含一篇详细介绍新模型架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

PRISM模型在序列建模中实现174倍吞吐量

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该集群包含一篇详细介绍新模型架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 Italiano(IT) · Jie Jiang, Ke Cheng, Xin Xu, Mengyang Pang, Tianhao Lu, Jiaheng Li, Yue Liu, Yuan Wang, Jun Zhang, Huan Yu, Zhouchen Lin ·

    PRISM：并行残差迭代序列模型

    arXiv:2602.10796v3 Announce Type: replace Abstract: Generative sequence modeling faces a fundamental tension between the expressivity of Transformers and the efficiency of linear sequence models. Existing efficient architectures are theoretically bounded by shallow, single-step l…

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