In huge nonconvex landscapes most critical points are saddle points, not local minima
burny_tech · x · 2026-10-15
burnytech notes that in enormous-dimensional nonconvex landscapes, most critical points are saddle points rather than local minima, and this worsens as dimensionality grows. It applies to LLM training and other deep learning systems: training is about passing through and escaping saddle-like regions instead of getting trapped in rare local minima. The post quotes roon's summary: "in high dimensional space hills are rare so you end up dying on saddle points."
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