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Flatland: The Adventures of Gradient Descent with Large Step Sizes

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arXiv:2606. 06722v1 Announce Type: new Abstract: The training of neural networks often entails objective functions that are not globally $L$-smooth.

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Authors: Leonardo Galli, Curtis Fox, Wiebke Bartolomaeus, Mark Schmidt, Holger Rauhut
Released: 2026-06-08 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Flatland: The Adventures of Gradient Descent with Large Step Sizes overlaps with clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)", experiment "#351 follow-up: broader-vocab position-0 sweep at T=1.0 + position-1 suffix isolation". Matching terms: rate. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.06722

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