English(EN) Graph Machine: Towards Better Pretraining via Edges

新的Graph Machine架构通过动态边路由增强AI预训练

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-03 04:00

研究人员推出了一种名为Graph Machine (GM) 的新颖架构，旨在通过利用边进行动态路由来改进预训练。与依赖固定大小状态或静态路由的现有方法不同，GM通过稀疏层和可微分更新指针的推荐机制，将状态复杂度保持在O(n)。在用GM稀疏层替换Qwen3-0.6B中75%的密集Transformer层并在15.7B token上进行预训练的测试中，该模型在损失方面仅有轻微下降，且token检索量极少，在检索更多token时损失略有改善。 AI

影响引入了一种新的架构方法，可能导致更大语言模型更有效和高效的预训练。

排序理由这是一篇详细介绍新AI架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

Qwen3-0.6B

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

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Lintai Hou · 2026-09-03 04:00

Graph Machine：通过边实现更好的预训练

arXiv:2609.02881v1 Announce Type: new Abstract: We introduce the Graph Machine (GM), an architecture that maintains an $O(n)$-sized state and accesses it through sparse, dynamic routing. Unlike methods with fixed-size states or sparse but static routing, GM preserves $O(n)$ compl…

本文如何被排名

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Graph Machine：通过边实现更好的预训练

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