Paper Summary -- torch.manual_seed(3407) is all you need

Whenever we train a neural network from scratch, it's weights are initialized with random values. So, if you re-run the same training job again and again, the values used to initialized the weights will keep on changing as they would be randomly generated. Now just imagine, metric of a State of the Art architecture for a given task is 80. You propose a new architecture for the same task and train your model from scratch. After you run it once (assuming all hyper-parameters were just perfect), you get 79.8 metric value.