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On Convergence of Lookahead in Smooth Games

Junsoo Ha · Gunhee Kim



A key challenge in smooth games is that there is no general guarantee for gradient methods to converge to an equilibrium. Recently, Chavdarova et al. (2021) reported a promising empirical observation that Lookahead (Zhang et al., 2019) significantly improves GAN training. While promising, few theoretical guarantees has been studied for Lookahead in smooth games. In this work, we establish the first convergence guarantees of Lookahead for smooth games. We present a spectral analysis and provide a geometric explanation of how and when it actually improves the convergence around a stationary point. Based on the analysis, we derive sufficient conditions for Lookahead to stabilize or accelerate the local convergence in smooth games. Our study reveals that Lookahead provides a general mechanism for stabilization and acceleration in smooth games.

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