English(EN) Model Capacity Determines Grokking through Competing Memorisation and Generalisation Speeds

模型容量在“领悟”中的作用通过竞争性记忆和泛化速度来解释

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-12 04:00

一篇新的研究论文提出了一个信息论框架来解释模型容量如何影响机器学习中的“领悟”（grokking）现象。研究表明，“领悟”现象，即模型在最初记忆训练数据后突然泛化，源于两个不同时间尺度的竞争：记忆速度和泛化速度。研究表明，这些时间尺度取决于模型的参数数量，较大的模型表现出更快的记忆速度。 AI

影响为理解模型规模如何影响泛化提供了理论框架，可能指导未来的模型开发。

排序理由学术论文，详细介绍了理解机器学习现象的新理论框架。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Yiding Song, Hanming Ye · 2026-10-12 04:00

模型容量决定了通过竞争性记忆和泛化速度实现的“过拟合”

arXiv:2605.09724v2 Announce Type: replace Abstract: Existing accounts of grokking explain the phenomena in terms of mechanistic frameworks such as circuit efficiency or lazy-to-rich transitions. However, despite a known dependence between grokking and model size, how model capaci…