Despite its flexibility to learn diverse inductive biases in machine learning programs, meta learning (i.e., learning to learn) has long been recognized to suffer from poor scalability due to its tremendous compute/memory costs, training instability, and a lack of efficient distributed training support. In this work, we focus on making scalable meta learning practical by introducing SAMA, which combines advances in both implicit differentiation algorithms and systems. Specifically, SAMA is designed to flexibly support a broad range of adaptive optimizers in the base level of meta learning programs, while reducing computational burden by avoiding explicit computation of second-order gradient information, and exploiting efficient distributed training techniques implemented for first-order gradients. Evaluated on multiple large-scale meta learning benchmarks, SAMA showcases up to 1.7/4.8x increase in throughput and 2.0/3.8x decrease in memory consumption respectively on single-/multi-GPU setups compared to other baseline meta learning algorithms. Furthermore, we show that SAMA-based data optimization leads to consistent improvements in text classification accuracy with BERT and RoBERTa large language models, and achieves state-of-the-art results in both small- and large-scale data pruning on image classification tasks, demonstrating the practical applicability of scalable meta learning across language and vision domains.

翻译：尽管元学习（即学会学习）在机器学习程序中具备学习多样归纳偏置的灵活性，但长期以来因其高昂的计算/内存成本、训练不稳定以及缺乏高效的分布式训练支持，被认为可扩展性较差。在本工作中，我们通过引入SAMA，结合隐式微分算法与系统两方面的进展，致力于使可扩展元学习走向实用化。具体而言，SAMA旨在灵活支持元学习程序基础层中广泛的适应性优化器，同时通过避免显式计算二阶梯度信息来降低计算负担，并利用针对一阶梯度实现的高效分布式训练技术。在多个大规模元学习基准上的评估表明，与其他基线元学习算法相比，SAMA在单GPU/多GPU配置下分别实现了最高1.7/4.8倍的吞吐量提升以及2.0/3.8倍的内存消耗降低。此外，我们展示了基于SAMA的数据优化在BERT和RoBERTa大语言模型的文本分类任务中带来了持续性的准确率提升，并在图像分类任务的小规模和大规模数据剪枝中均取得了最先进结果，这证明了可扩展元学习在语言与视觉领域的实用适用性。