English(EN) Fast and Stable Triangular Inversion for Delta-Rule Linear Transformers

新方法加速线性Transformer的三角求逆

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

arXiv上发表的一篇新论文介绍了一种改进的三角矩阵求逆方法，这是Delta-Rule线性Transformer中的关键操作。该技术对于Qwen3.5/3.6、Kimi Linear/K3和RWKV-7等高效长上下文模型至关重要。研究侧重于提高数值稳定性和计算效率，特别是针对低精度浮点表示。实验表明，与现有的SGLang实现相比，三角矩阵求逆速度最多可提高4.3倍，在保持模型精度的同时，在层级别上实现了显著的性能提升。 AI

影响提高了长上下文LLM的效率和稳定性，可能实现更快的推理和训练。

排序理由研究论文，详细介绍了一种用于线性Transformer核心组件的新算法方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Aleksandros Sobczyk, Gioele Gottardo, Christos K. Matzoros, Mirko De Vita, Filip Skogh, Anastasios Zouzias, Jiawei Zhuang · 2026-11-05 05:00

用于 Delta-Rule 线性 Transformer 的快速稳定三角求逆

arXiv:2605.21325v2 Announce Type: replace Abstract: Linear attention has emerged as a cornerstone for efficient long-context architectures, as evidenced by its integration into state-of-the-art open-source models including Qwen3.5/3.6, Kimi Linear/K3, and RWKV-7. Models that inco…

本文如何被排名

报道来源 [1]

用于 Delta-Rule 线性 Transformer 的快速稳定三角求逆

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