Grokking in modular addition: three stages to generalization
A post dated October 4, 2025 describes a paper proposing three stages of grokking in modular addition and a data threshold near n log n, with weight decay and moderate width supporting feature learning.
Source: Uma explicação em três etapas do grokking em adição modular (x.com). Text prepared with AI from this source.
What happened and what to do
On October 4, 2025, the post described a paper on grokking in modular addition tasks. Its explanation proposes three stages and states that generalization may emerge with roughly the group size multiplied by the logarithm of the group size in examples. The text also highlights weight decay and moderate width as conditions that support feature learning.
A company training models could test these hypotheses in controlled experiments: vary the amount of data, model width, and weight decay, then track the gap between training performance and generalization. An experimentation pipeline and dashboard could record configurations, metrics, and outcomes to inform training decisions, without assuming that a threshold observed in modular addition applies to other tasks.
How the consultancy can help
Wendelmaques can diagnose the opportunity for experimentation, define metrics, and design a test plan; it can then implement and operate the experiment pipeline and dashboard on the client's own infrastructure.
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