English(EN) Output Embedding Centering for Stable LLM Pretraining

新方法通过解决输出嵌入几何问题来稳定大语言模型预训练

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-11 04:00

研究人员推出了一种名为输出嵌入中心化（OEC）的新策略，以稳定大语言模型（LLMs）的预训练。该方法通过分析输出嵌入的几何形状并确定各向异性嵌入是根本原因，从而解决了输出logit发散这一常见的训练不稳定性问题。OEC可以实现为一种称为μ-centering的确定性操作，或一种称为μ-loss的正则化技术，在实验测试中，这两种方法都表现出优于z-loss等现有方法的性能。 AI

影响提供了一种更稳定且可能对超参数不那么敏感的大语言模型预训练方法，这有望降低训练成本并提高模型质量。

排序理由详细介绍大语言模型预训练新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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

本文如何被排名

Signal score: 12 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 详细介绍大语言模型预训练新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, infra; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Breaking (< 6h); Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Felix Stollenwerk, Anna Lokrantz, Niclas Hertzberg · 2026-09-11 04:00

用于稳定大语言模型预训练的输出嵌入中心化

arXiv:2601.02031v3 Announce Type: replace-cross Abstract: Pretraining of large language models is not only expensive but also prone to certain training instabilities. A specific instability that often occurs at the end of training is output logit divergence. The most widely used …

本文如何被排名

报道来源 [1]

用于稳定大语言模型预训练的输出嵌入中心化

相关实体

相关话题