I think the concept makes sense. The basic insight, that the right batch size depends on the difficulty and noisiness of a task, is already used by teams. For example, the PaLM paper from last week increased its batch size throughout training.
But as far as I know, the more precise predictions of optimal batch size aren't used much, probably because it's expensive to measure accurately, or because the predictive equation isn't accurate enough to begin with. I wonder if we can "transfer" the optimal batch size from a smaller setting (smaller model or data) to the full setting, like in our paper. This would make it much more practical.
I think there remains an immense amount of such suboptimality still hanging from the tree, so to speak.
For example, our recent paper "Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer"[1] shows that even learning rate and initialization used by existing models are deeply wrong. By just picking them correctly (which involves some really beautiful mathematics), we can effectively double the model size of the GPT-3 6.7B model (to be comparable in quality to the 13B model across the suite of benchmark tasks).
Large neural networks behave in a way we are only beginning to understand well just because each empirical probe of any such model is so much more expensive and time consuming than typical models. But principled theory here can have a lot of leverage by pointing out the right direction to look, as it did in our work.
But as far as I know, the more precise predictions of optimal batch size aren't used much, probably because it's expensive to measure accurately, or because the predictive equation isn't accurate enough to begin with. I wonder if we can "transfer" the optimal batch size from a smaller setting (smaller model or data) to the full setting, like in our paper. This would make it much more practical.