Graph Machine: Towards Better Pretraining via Edges

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 $O(n)$ complexity in its sparse layers without restricting the potentially accessible state size to $O(1)$. Instead, GM uses edges - pointer-like objects updated differentiably by a referral mechanism resembling pointer chasing. We replace 75% of the dense Transformer layers in Qwen3-0.6B with GM sparse layers and pretrain from scratch on 15.7B tokens. With only 2 of 4,096 tokens retrieved per KV head in each sparse layer, loss degrades only slightly; with 4, the best model marginally improves loss.

Machine Learning (cs.LG)

arXiv:2609.02881 [cs.LG]

(or arXiv:2609.02881v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.02881 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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