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Local exponential stability of mean-field Langevin descent-ascent and associated particle system

arXiv 2602.01564 ↗First posted 2 Feb 2026cs.LG math.AP math.OCcited by 0

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Abstract (arXiv)

We study the mean-field Langevin descent-ascent (MFL-DA), a coupled optimization dynamics on the space of probability measures for entropically regularized two-player zero-sum games, together with its associated interacting particle system. For general nonconvex-nonconcave payoffs, Wang and Chizat (COLT 2024) asked whether the original single-timescale MFL-DA converges to the mixed Nash equilibrium and, if so, at what rate. We prove a local affirmative answer in Wasserstein space: if the initial datum is sufficiently close to the mixed Nash equilibrium, then the mean-field dynamics converges to it exponentially fast at a quantitative rate. We further show that the finite-N particle system inherits this stability up to times exponential in N, with an N-independent exponential rate modulo a finite-particle error floor. Combined with the recent counterexample of Mourrat and Pillaud-Vivien for MFL-DA, which shows that global convergence cannot hold in general, our theorem completes the positive local counterpart of the Wang-Chizat question: the mixed Nash equilibrium has a robust basin of attraction, stable under both the mean-field flow and its finite-particle approximation.

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References · 16

  1. 2018A Mean Field View of the Landscape of Two-Layers Neural Networkscited by 463
  2. 2018On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transportcited by 422
  3. 2020The Complexity of Constrained Min-Max Optimizationcited by 81
  4. 2022Mean-Field Langevin Dynamics: Exponential Convergence and Annealingcited by 60
  5. 2018Finding Mixed Nash Equilibria of Generative Adversarial Networkscited by 48
  6. 2019On Finding Local Nash Equilibria (and Only Local Nash Equilibria) in Zero-Sum Gamescited by 35
  7. 2020A mean-field analysis of two-player zero-sum gamescited by 28
  8. 2023Convergence of mean-field Langevin dynamics: Time and space discretization, stochastic gradient, and variance reductioncited by 19
  9. 2022An Exponentially Converging Particle Method for the Mixed Nash Equilibrium of Continuous Gamescited by 14
  10. 2022Two-Scale Gradient Descent Ascent Dynamics Finds Mixed Nash Equilibria of Continuous Games: A Mean-Field Perspectivecited by 13
  11. 2023Symmetric Mean-field Langevin Dynamics for Distributional Minimax Problemscited by 11
  12. 2022Provably convergent quasistatic dynamics for mean-field two-player zero-sum gamescited by 9
4 more
  1. 2024Coupled Wasserstein Gradient Flows for Min-Max and Cooperative Gamescited by 4
  2. 2024On the Convergence of Min-Max Langevin Dynamics and Algorithmcited by 3
  3. 2025Convergence of Time-Averaged Mean Field Gradient Descent Dynamics for Continuous Multi-Player Zero-Sum Gamescited by 2
  4. 2025On gradient descent-ascent flows in metric spacescited by 1

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