Title: AutoSparse: Towards Automated Sparse Training. Abstract: Sparse training is emerging as a promising avenue for reducing the computational cost of training neural networks. Several recent studies have proposed pruning methods using learnable thresholds to efficiently explore the non-uniform distribution of sparsity inherent within the models. In this paper, we propose Gradient Annealing (GA), a gradient driven approach where gradients to pruned out weights are scaled down in a non-linear manner. GA eliminates the need for additional sparsity-inducing regularization by providing an elegant trade-off between sparsity and accuracy. We integrated GA with the latest learnable threshold based pruning methods to create an automated sparse training algorithm called AutoSparse. Our algorithm achieves state-of-the-art accuracy with 80% sparsity for ResNet50 and 75% sparsity for  MobileNetV1 on Imagenet-1K. AutoSparse also results in 7× reduction in inference FLOPS and > 2× reduction in training FLOPS for ResNet50 on ImageNet at 80% sparsity. Finally, GA generalizes well to fixed-budget (Top-K, 80%) sparse training methods, improving the accuracy of ResNet50 on Imagenet-1K, to outperform TopKAST+PP by 0.3%.