This explanation of why larger batches might make optimization harder makes a fair amount of sense to me. This comment on Quora helps a bit as well - with a sl

This explanation of why larger batches might make optimization harder makes a fair amount of sense to me. This comment on Quora helps a bit as well - with a slightly different approach to characterizing the trade off of batch size/accuracy/runtime performance. https://www.quora.com/Intuitively-how-does-mini-batch-size-affect-the-performance-of-stochastic-gradient-descent https://www.quora.com/Intuitively-how-does-mini-batch-size-a... I wonder if rather than computing a single gradient for a large batch you could simultaneously compute a gradient for the batch and for several subsets of the batch -- then pick or combine the gradient subset(s) that most differ from the full batch result. Not sure if that would work out to a computational efficiency gain. Are there any optimizers that dynamically scale the batch size up/down based on an online metric?