larger learning rate + large weight decay performs better?

[askerlee]

Hi all,
My colleague and I tried a combination of (relatively) large Ranger learning rate (say, 0.001) + large weight decay (say, 0.1). Seems the large decay leads to better performance? We tried two different models, and observed 0.5-1.5% increase of ImageNet classification accuracy, but both models were customized models, and not standard ones like Resnet.
Not sure whether anyone else finds similar results.