How AI training scales

# How AI training scales Source: [https://openai.com/index/how-ai-training-scales/](https://openai.com/index/how-ai-training-scales/) We’ve discovered that the gradient noise scale, a simple statistical metric, predicts the parallelizability of neural network training on a wide range of tasks\. Since complex tasks tend to have noisier gradients, increasingly large batch sizes are likely to become useful in the future, removing one potential limit to further growth of AI systems\. More broadly, these results show that neural network training need not be considered a mysterious art, but can be rigorized and systematized\. We’ve observed several patterns in the gradient noise scale which offer clues as to what the future of AI training may hold\. First, in our experiments, the noise scale typically increases by an order of magnitude or more over the course of training\. Intuitively, this means the network learns the more “obvious” features of the task early in training and learns more intricate features later\. For example, in the case of an image classifier, the network might first learn to identify small\-scale features such as edges or textures that are present in most images, while only later putting these pieces together into more general concepts such as cats and dogs\. To see the full variety of edges or textures, the network only needs to see a small number of images, so the noise scale is smaller; once the network knows more about larger objects, it can process many more images at once without seeing duplicative data\. We see some[preliminary indications⁠\(opens in a new window\)](https://arxiv.org/pdf/1812.06162.pdf)that the same effect holds across different models on the same dataset—more powerful models have a higher gradient noise scale, but only because they achieve a lower loss\. Thus, there’s some evidence that the increasing noise scale over training isn’t just an artifact of convergence, but occurs because the model gets better\. If this is true, then we expect future, more powerful models to have higher noise scale and therefore be more parallelizable\. Second, tasks that are subjectively more difficult are also more amenable to parallelization\. In the context of supervised learning, there is a clear progression from MNIST, to SVHN, to ImageNet\. In the context of reinforcement learning, there is a clear progression from Atari Pong to[Dota 1v1⁠\(opens in a new window\)](https://blog.openai.com/dota-2/)to[Dota 5v5⁠\(opens in a new window\)](https://blog.openai.com/openai-five/), with the optimal batch sizes differing by a factor of more than 10,000\. Thus, as AI advances to new and more difficult tasks, we expect models to tolerate higher batch size\.