Applying meta-learning algorithms to large foundation models, with their vast parameter spaces, presents substantial computational and memory difficulties. The methods discussed previously, while effective in principle, often become impractical due to resource constraints when scaled to models containing billions of parameters. Calculating meta-gradients, especially second-order ones like those in standard MAML (e.g., involving terms related to $\nabla^2_{\theta} \mathcal{L}_{\text{meta}}$), can exceed the memory capacity of typical hardware.

This chapter focuses on practical strategies to make meta-learning feasible for these large-scale models. We will examine the specific sources of computational bottlenecks, particularly in gradient-based meta-learning. You will learn about memory optimization techniques such as gradient checkpointing and mixed-precision training. We also cover distributed training paradigms suitable for meta-learning across multiple GPUs or nodes. Furthermore, we look into efficient task sampling and batching methods, approximation techniques that reduce computational load, and approaches for benchmarking the performance and resource usage of scaled meta-learning implementations.