Paper: Midtraining Bridges Pretraining and Posttraining Distributions
XiongChenyan · x · 2026-08-16
The paper 'Midtraining Bridges Pretraining and Posttraining Distributions' explores the mathematical foundations of training progression from a deep manifold perspective. It reveals that altering data mixtures alone does not determine training outcomes; the same specialized data mixture can be beneficial at one stage and harmful at another. The study analyzes four dimensions: data complexity, boundary conditions, training dynamics, and manifold homology, noting that introducing code or mathematical data alters the learning space.
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