Summary
The lookahead method provides an interesting way to accelerate and stabilize algorithms of stochastic gradient descent family. Main idea is quite simple:
 Set some number . Take initial parameter weights
 Do steps with your favorite optimization algorithm:

Take some value between initial and :
 Update with the last output of the algorithm.
 Repeat
profit
Authors introduced separation on the fast weights and slow weights, which naturally arises in the described procedure. The paper contains proof for optimal stepsize of the quadratic loss function and provides understanding why this technique could reduce variance of Stochastic gradient descent in the noisy quadratic case. Moreover, this work compares the convergence rate in dependency of condition number of the squared system.
It is worth to say, that autor claims significant improvement in practical huge scale settings (ImageNet, CIFAR10,CIFAR100)
Pros
 Interesting idea, costs almost nothing, why not to try?
 Works with any SGDlike optimizer (SGD, Adam, RmsProp)
 Analytical approach to quadratic case.
 Wide set of empirical tests (Image classifiaction, Neural Translation, LSTM training)
Cons
 Lack of test loss pictures, the majority of them obtained for the train loss\accuracy
 Lack of pictures with different batch sizes
 Difficult to analyze the method analytically