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A First-Order Mean Field Control Analysis of Transformer Layers under Cross-Entropy Training
[Submitted on 22 Jun 2026] · 2026-06-23 · via stat.ML updates on arXiv.org

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Abstract:We study Transformer-type residual layers under cross-entropy training through a continuous-depth mean field control viewpoint. Depth is treated as time, layer parameters as controls, and the residual Transformer recursion as an explicit Euler scheme for a controlled hidden-state flow. For fixed controls, we prove an $O(\varepsilon)$ pathwise approximation of finite-depth trajectories by the continuous flow and combine this with high-probability sampling bounds for the empirical cross-entropy risk. We formulate the limiting population problem as a first-order transport control problem for the law of hidden states and derive a Pontryagin condition whose terminal adjoint contains the softmax residual. We also give finite-class and metric-entropy uniform estimates, compare optimal values, and discuss existence, stability, continuous-to-discrete recovery, initialization, and range estimates for continuous minimizers.

Submission history

From: Hongwei Yuan [view email]
[v1] Mon, 22 Jun 2026 12:21:37 UTC (44 KB)

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