Higher is a library enabling higher-order optimization over training loops, specifically for meta-learning applications. It makes torch.nn.Module instances stateless so parameter changes can be tracked and gradients taken through intermediate parameters. The library provides differentiable optimizers to facilitate implementing meta-learning approaches like MAML (Model-Agnostic Meta-Learning), allowing computation of gradients with respect to initial parameters through multiple optimization steps.