Nonlinear Two-Time-Scale Stochastic Approximation: A Sharp Phase Transition and How to Beat It

arXiv:2606.14488v1 Announce Type: cross Abstract: Recent finite-time analyses of nonlinear two-time-scale stochastic approximation show that under contractive assumptions the slow iterate $Y_k$ with stepsizes $\beta_k=\Theta(k^{-1})$ and $\alpha_k=\Theta(k^{-a})$, $a\in(1/2,1)$, generally satisfies a mean-square rate of order $k^{-a}$; decoupled $k^{-1}$ rates require strong local linearity. We identify a sharp regularity-dependent boundary. In a rate-determining normal form where the slow drift contains a locally linear leakage and a nonlinear remainder of order $1+\rho$ ($\rho\in[0,1]$), the
This is a theoretical paper in stochastic approximation, not directly linked to current industry or geopolitical events.
This type of research is foundational for improving AI algorithms but does not immediately impact strategic decision-making.
No immediate changes for strategic readers; this is an incremental advancement in highly technical AI theory.
Further theoretical understanding of stochastic approximation algorithms.
Potential for marginal improvements in the efficiency or robustness of some AI models over the very long term.
Extremely distant and indirect influence on specific AI applications if these theoretical gains aggregate significantly.
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