新的谱框架增强了生成基础模型的扩展性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

研究人员开发了一个谱框架，以应对扩展生成基础模型所面临的挑战，特别是在不同宽度和深度下实现稳定的特征学习和超参数迁移。该框架统一了现有的最大更新参数化（$\mu$P）公式，并指出CompleteP风格的参数化更适合Transformer等架构。在GPT-2风格模型上的实验表明，与标准的或Depth-$\mu$P风格的参数化不同，这种CompleteP风格的参数化确保了稳定的学习和鲁棒的超参数迁移。 AI

影响为改进大规模生成模型的稳定性和超参数可迁移性提供了一种理论和实践方法。

排序理由这是一篇研究论文，详细介绍了改进模型扩展性的一项新理论框架和实验验证。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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 stat.ML TIER_1 English(EN) · Chenyu Zheng, Rongzhen Wang, Xinyu Zhang, Chongxuan Li · 2026-11-02 05:00

Spectral Condition for $\mu$P under Width-Depth Scaling

arXiv:2603.00541v3 Announce Type: replace-cross Abstract: Generative foundation models are increasingly scaled in both width and depth, posing significant challenges for stable feature learning and reliable hyperparameter (HP) transfer across model sizes. While maximal update par…

本文如何被排名

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Spectral Condition for $\mu$P under Width-Depth Scaling

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