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The Lookahead optimizer improves the training stability of deep neuralnetworks by having a set of fast weights that "look ahead" to guide the descentdirection. Here, we combine this idea with sharpness-aware minimization (SAM)to stabilize its multi-step variant and improve the loss-sharpness trade-off.We propose Lookbehind, which computes $k$ gradient ascent steps ("lookingbehind") at each iteration and combine the gradients to bias the descent steptoward flatter minima. We apply Lookbehind on top of two popularsharpness-aware training methods -- SAM and adaptive SAM (ASAM) -- and showthat our approach leads to a myriad of benefits across a variety of tasks andtraining regimes. Particularly, we show increased generalization performance,greater robustness against noisy weights, and higher tolerance to catastrophicforgetting in lifelong learning settings.

Lookbehind Optimizer: k steps back, 1 step forward