  A core issue with learning to optimize neural networks has been the lack of
generalization to real world problems. To address this, we describe a system
designed from a generalization-first perspective, learning to update optimizer
hyperparameters instead of model parameters directly using novel features,
actions, and a reward function. This system outperforms Adam at all neural
network tasks including on modalities not seen during training. We achieve 2x
speedups on ImageNet, and a 2.5x speedup on a language modeling task using over
5 orders of magnitude more compute than the training tasks.
