Understanding Dynamics of Adam in Zero-Sum Games: An ODE Approach

• URL: http://arxiv.org/abs/2605.19392v1
• Date: Tue, 19 May 2026 05:38:47 GMT
• Title: Understanding Dynamics of Adam in Zero-Sum Games: An ODE Approach
• Abstract summary: We derive ordinary differential equations that serve as continuous-time limits of the Adam-DA.<n>Our analysis reveals that the roles of the first- and second-order momentum parameters in zero-sum games are exactly the opposite of their well-documented effects in minimization problems.
• Score: 14.605735105736864
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The remarkable success of the Adam in training neural networks has naturally led to the widespread use of its descent-ascent counterpart, Adam-DA, for solving zero-sum games. Despite its popularity in practice, a rigorous theoretical understanding of Adam-DA still lags behind. In this paper, we derive ordinary differential equations (ODEs) that serve as continuous-time limits of the Adam-DA. These ODEs closely approximate the discrete-time dynamics of Adam-DA, providing a tractable analytical framework for understanding its behavior in zero-sum games. Using this ODE approach, we investigate two fundamental aspects of Adam-DA: local convergence and implicit gradient regularization. Our analysis reveals that the roles of the first- and second-order momentum parameters in zero-sum games are exactly the opposite of their well-documented effects in minimization problems. We validate these predictions through GAN experiments across multiple architectures and datasets, demonstrating the practical implications of this reversed momentum effect.

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