English

Steering Dynamical Regimes of Diffusion Models by Breaking Detailed Balance

Statistical Mechanics 2026-02-19 v1 Machine Learning

Abstract

We show that deliberately breaking detailed balance in generative diffusion processes can accelerate the reverse process without changing the stationary distribution. Considering the Ornstein--Uhlenbeck process, we decompose the dynamics into a symmetric component and a non-reversible anti-symmetric component that generates rotational probability currents. We then construct an exponentially optimal non-reversible perturbation that improves the long-time relaxation rate while preserving the stationary target. We analyze how such non-reversible control reshapes the macroscopic dynamical regimes of the phase transitions recently identified in generative diffusion models. We derive a general criterion for the speciation time and show that suitable non-reversible perturbations can accelerate speciation. In contrast, the collapse transition is governed by a trace-controlled phase-space contraction mechanism that is fixed by the symmetric component, and the corresponding collapse time remains unchanged under anti-symmetric perturbations. Numerical experiments on Gaussian mixture models support these findings.

Keywords

Cite

@article{arxiv.2602.15914,
  title  = {Steering Dynamical Regimes of Diffusion Models by Breaking Detailed Balance},
  author = {Haiqi Lu and Ying Tang},
  journal= {arXiv preprint arXiv:2602.15914},
  year   = {2026}
}