Meta-learning aims to leverage information across related tasks to improve prediction on unlabeled data for new tasks when only a small number of labeled observations are available ("few-shot" learning). Increased task diversity is often believed to enhance meta-learning by providing richer information across tasks. However, recent work by Kumar et al. (2022) shows that increasing task diversity, quantified through the overall geometric spread of task representations, can in fact degrade meta-learning prediction performance across a range of models and datasets. In this work, we build on this observation by showing that meta-learning performance is affected not only by the overall geometric variability of task parameters, but also by how this variability is allocated relative to an underlying low-dimensional structure. Similar to Pimonova et al. (2025), we decompose task-specific regression effects into a structurally informative component and an orthogonal, non-informative component. We show theoretically and through simulation that meta-learning prediction degrades when a larger fraction of between-task variability lies in orthogonal, non-informative directions, even when the overall geometric variability of tasks is held fixed.

翻译：元学习旨在利用相关任务间的信息，以在仅有少量标注观测可用（"小样本"学习）的情况下提升对新任务未标注数据的预测能力。通常认为，增加任务多样性可通过提供更丰富的跨任务信息来增强元学习效果。然而，Kumar等人（2022）的最新研究表明，通过任务表示的整体几何分散度来量化的任务多样性增加，实际上可能在一系列模型和数据集中降低元学习的预测性能。本研究基于这一观察，进一步证明元学习性能不仅受任务参数整体几何变异性的影响，还取决于这种变异性相对于底层低维结构的分配方式。与Pimonova等人（2025）的研究类似，我们将任务特定的回归效应分解为结构信息性成分和正交的非信息性成分。通过理论分析和仿真实验，我们证明当任务间变异性中更大比例存在于正交的非信息性方向时，即使任务的整体几何变异性保持不变，元学习的预测性能也会下降。