Title: Adaptive Dataset Sampling by Deep Policy Gradient. Abstract: Mini-batch SGD is a predominant optimization method in deep learning. Several works aim to improve naïve random dataset sampling, which appears typically in deep learning literature, with additional prior to allow faster and better performing optimization. This includes, but not limited to, importance sampling and curriculum learning. In this work, we propose an alternative way: we think of sampling as a trainable agent and let this external model learn to sample mini-batches of training set items based on the current status and recent history of the learned model. The resulting adaptive dataset sampler, named RLSampler, is a policy network implemented with simple recurrent neural networks trained by a policy gradient algorithm. We demonstrate RLSampler on image classification benchmarks with several different learner architectures and show consistent performance gain over the originally reported scores. Moreover, either a pre-sampled sequence of indices or a pre-trained RLSampler turns out to be more effective than naïve random sampling regardless of the network initialization and model architectures. Our analysis reveals the possible existence of a model-agnostic sample sequence that best represents the dataset under mini-batch SGD optimization framework.