English(EN) SCOUT: Symmetric Consensus Outlier Detection for Failure Localization in LLM Pre-Training

新的SCOUT框架可检测LLM预训练中的故障

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-12 04:00

研究人员开发了SCOUT，一个旨在精确定位大型语言模型（LLM）预训练期间故障的新框架。SCOUT通过在等效副本之间建立严格的多数共识来识别异常值，从而定位停顿、减速或数值错误的根源。即使在训练过程挂起时，该系统也能保持响应，并且其回放功能有助于检测静默数据损坏并验证检查点完整性。SCOUT与PyTorch、TorchTitan、Megatron-Core和DeepSpeed等流行框架兼容。 AI

影响通过实现更快、更准确的故障诊断，提高了LLM预训练的效率和可靠性。

排序理由该集群描述了arXiv论文中提出的一个用于LLM预训练故障定位的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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Newsworthiness bucket: Tool; 该集群描述了arXiv论文中提出的一个用于LLM预训练故障定位的新框架。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Zhuang Wang · 2026-08-12 04:00

SCOUT：用于大语言模型预训练故障定位的对称共识异常值检测

arXiv:2608.11034v1 Announce Type: cross Abstract: In LLM pre-training, synchronization propagates rank-local stalls, slowdowns, and numerical errors into job-wide symptoms, obscuring their origin. Existing diagnosis often relies on in-process monitors that cannot report after the…

本文如何被排名

报道来源 [1]

SCOUT：用于大语言模型预训练故障定位的对称共识异常值检测

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