In this paper, we propose a generic Runtime Network Routing (RNR) framework for efficient image classification, which selects an optimal path inside the network. Unlike existing static neural network acceleration methods, our method preserves the full ability of the original large network and conducts dynamic routing at runtime according to the input image and current feature maps. The routing is performed in a bottom-up, layer-by-layer manner, where we model it as a Markov decision process and use reinforcement learning for training. The agent determines the estimated reward of each sub-path and conducts routing conditioned on different samples, where a faster path is taken when the image is easier for the task. Since the ability of network is fully preserved, the balance point is easily adjustable according to the available resources. We test our method on both multi-path residual networks and incremental convolutional channel pruning, and show that RNR consistently outperforms static methods at the same computation complexity on both the CIFAR and ImageNet datasets. Our method can also be applied to off-the-shelf neural network structures and easily extended to other application scenarios.