As the scale of machine learning models increases, trends such as scaling laws anticipate consistent downstream improvements in predictive accuracy. However, these trends take the perspective of a single model-provider in isolation, while in reality providers often compete with each other for users. In this work, we demonstrate that competition can fundamentally alter the behavior of these scaling trends, even causing overall predictive accuracy across users to be non-monotonic or decreasing with scale. We define a model of competition for classification tasks, and use data representations as a lens for studying the impact of increases in scale. We find many settings where improving data representation quality (as measured by Bayes risk) decreases the overall predictive accuracy across users (i.e., social welfare) for a marketplace of competing model-providers. Our examples range from closed-form formulas in simple settings to simulations with pretrained representations on CIFAR-10. At a conceptual level, our work suggests that favorable scaling trends for individual model-providers need not translate to downstream improvements in social welfare in marketplaces with multiple model providers.

翻译：随着机器学习模型规模的扩大，缩放律等趋势预测预测准确性会持续提升。然而，这些趋势仅从单一模型提供者的孤立视角出发，而现实中提供者之间往往存在用户竞争。本研究表明，竞争可能从根本上改变这些缩放趋势的行为，甚至导致用户整体的预测准确性随规模增大呈现非单调性或下降趋势。我们定义了分类任务的竞争模型，并以数据表征作为研究规模扩大影响的切入点。研究发现，在许多场景中，提升数据表征质量（以贝叶斯风险衡量）会降低竞争模型提供者市场中用户的整体预测准确性（即社会福利）。我们的案例涵盖从简单场景下的闭式公式到基于CIFAR-10预训练表征的仿真实验。在概念层面，本研究揭示：个体模型提供者有利的缩放趋势，未必能在多模型提供者市场中转化为社会福利的下游提升。