Papers › Lookahead Optimizer: k steps forward, 1 step back
Lookahead Optimizer: k steps forward, 1 step back
Michael R. Zhang, James Lucas, Geoffrey Hinton, Jimmy Ba
The vast majority of successful deep neural networks are trained using variants of stochastic gradient descent (SGD) algorithms. Recent attempts to improve SGD can be broadly categorized into two approaches: (1) adaptive learning rate schemes, such as AdaGrad and Adam, and (2) accelerated schemes, such as heavy-ball and Nesterov momentum. In this paper, we propose a new optimization algorithm, Lookahead, that is orthogonal to these previous approaches and iteratively updates two sets of weights. Intuitively, the algorithm chooses a search direction by looking ahead at the sequence of fast weights generated by another optimizer. We show that Lookahead improves the learning stability and lowers the variance of its inner optimizer with negligible computation and memory cost. We empirically demonstrate Lookahead can significantly improve the performance of SGD and Adam, even with their default hyperparameter settings on ImageNet, CIFAR-10/100, neural machine translation, and Penn Treebank.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Stochastic Optimization | CIFAR-10 ResNet-18 - 200 Epochs | Lookahead | Accuracy | 95.27 | #2 of 4 | Archive leaderboard | report |
| Stochastic Optimization | CIFAR-10 ResNet-18 - 200 Epochs | SGD | Accuracy | 95.23 | #3 of 4 | Archive leaderboard | report |
| Stochastic Optimization | CIFAR-10 ResNet-18 - 200 Epochs | ADAM | Accuracy | 94.84 | #4 of 4 | Archive leaderboard | report |
| Stochastic Optimization | ImageNet ResNet-50 - 50 Epochs | Lookahead | Top 1 Accuracy | 75.13% | #1 of 2 | Archive leaderboard | report |
| Stochastic Optimization | ImageNet ResNet-50 - 50 Epochs | SGD | Top 5 Accuracy | 92.15% | #2 of 2 | Archive leaderboard | report |
| Stochastic Optimization | ImageNet ResNet-50 - 60 Epochs | Lookahead | Top 1 Accuracy | 75.49% | #1 of 2 | Archive leaderboard | report |
| Stochastic Optimization | ImageNet ResNet-50 - 60 Epochs | Lookahead | Top 5 Accuracy | 92.53 | #1 of 2 | Archive leaderboard | report |
| Stochastic Optimization | ImageNet ResNet-50 - 60 Epochs | SGD | Top 1 Accuracy | 75.15% | #2 of 2 | Archive leaderboard | report |
| Stochastic Optimization | ImageNet ResNet-50 - 60 Epochs | SGD | Top 5 Accuracy | 92.56 | #2 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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