GASLoC解决了分布式LLM训练中通信效率与异构带宽的痛点，做大规模模型预训练的团队可以直接参考实验对比，看看能否替代现有方案。
GASLoC是一种新型去中心化预训练算法，旨在解决LLM训练中通信效率低下的问题。传统方法依赖同步All-Reduce操作，在带宽或工作速度不均时成为瓶颈。GASLoC通过将通信加速泛化到“外部优化器”，实现了兼容自适应优化器、支持本地优化步骤和稀疏随机通信的实用gossip训练框架。实验表明，在单步通信设置下，GASLoC在多种拓扑结构中优于现有去中心化算法；在多步本地更新时，性能与DiLoCo相当，且在异构带宽场景下显著超越DiLoCo。
Unifying Local Communications and Local Updates for LLM Pretraining
Communication-efficient pre-training of LLMs is increasingly important as training draws on compute distributed across clusters, data centers, and lower-bandwidth links. Many practical methods reduce communication frequency but still rely on synchronous All-Reduce operations that maintain identical model states and tie progress to global collectives. This can become a bottleneck when bandwidth or worker speed is heterogeneous. We introduce GASLoC, a novel decentralized pre-training algorithm that generalizes the notion of communication acceleration to the recently popular "outer optimizer" to allow a practical gossip-based training framework that is compatible with adaptive optimizers, allows for local optimizer steps, and can utilize sparse randomized peer communication. Empirically, on a number of standard LLM training tasks, we demonstrate that GASLoC outperforms state-of-the-art decentralized algorithms in single step per communication setting for a number of topologies and, unlike existing decentralized methods in the LLM setting, it allows to obtain performance competitive with DiLoCo when utilizing multiple local steps. In the heterogeneous bandwidth setting we demonstrate the advantage of GASLoC showing that it can significantly outperform DiLoCo.