English(EN) Exact Scaling Laws for a Random-Features Bigram Model

研究人员为瓶颈式大模型推导出精确标度律

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员开发了一个具有幂律标记统计的大模型，用于解析研究神经网络标度律。该模型使用固定的随机嵌入作为表征能力的代理，可以根据模型宽度、词汇量大小和齐夫指数推导出预测遗憾的标度律。理论发现与近期关注优化动态的论述一致，并表明重尾语言统计可能是优化驱动和容量驱动标度的根本原因。在WikiText2和OpenWebText等自然语言数据集上的实验结果与预测的标度曲线非常吻合。 AI

影响为语言模型的标度行为提供了理论见解，可能指导未来的模型开发。

排序理由学术论文，详细介绍了语言模型标度律的理论和实验结果。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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Newsworthiness bucket: Tool; 学术论文，详细介绍了语言模型标度律的理论和实验结果。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.LG TIER_1 English(EN) · Julian Enoch, Elvis Dohmatob · 2026-10-15 04:00

Exact Scaling Laws for a Random-Features Bigram Model

arXiv:2610.19281v1 Announce Type: new Abstract: Understanding neural scaling laws is increasingly important for theory and practice, yet first-principles explanations remain limited even in simple settings. In this work, we study capacity-limited scaling in a bottlenecked bigram …

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Exact Scaling Laws for a Random-Features Bigram Model

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