Grokking 现象解析:什么条件触发延迟泛化、平台期有多长
Statistical Vignettes on the Grokking Phenomenon
Grokking 不玄了:这篇论文给出触发条件和平台期长度公式,还配了实验验证,做理论的可对照。
这篇 arXiv 论文在一个可解析求解的简单设定中研究 Grokking 现象,把初始化、信号强度、权重衰减、样本量和输入维度五个因素的影响拆分开。作者区分了经由冗余特征的早期拟合、核心特征学习和延迟泛化三个阶段,并给出 Grokking 可证明发生的尖锐条件。论文还推导出泛化平台长度的解析渐近表达式,同时刻画了早期泛化以及无法达到指定测试精度目标的区间。分析表明插值边界是一个独立动力学区间,其谱隙消失使核心学习与泛化分离;整体图景类似化学反应动力学中的快慢两相,也与文献中的 lazy-to-rich 转变相呼应。
Statistical Vignettes on the Grokking Phenomenon
In this work, we study grokking in a simple, analytically tractable setting whose rich phenomenology allows us to disentangle the effects of initialization, signal strength, weight decay, sample size, and input dimension. These factors can produce superficially similar learning curves through distinct mechanisms, complicating their interpretation. Our analysis distinguishes early fitting through nuisance features, core feature learning, and delayed generalization. We identify sharp conditions under which grokking provably occurs and derive analytic asymptotic expressions for the lengths of the generalization plateaus. Our results also characterize regimes of early generalization and failure to reach a prescribed test accuracy target. We show that the interpolation edge constitutes a distinct dynamical regime, where vanishing spectral gaps separate core learning from generalization, helping clarify the role of proximity to interpolation in existing accounts of grokking. The resulting picture is analogous to fast and slow phases in chemical kinetics and suggests that the lazy-to-rich transition discussed in the literature may reflect a similar separation of dynamical time scales. Our theoretical results are faithfully supported by extensive experiments.