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，尺度律等趋势预期预测准确性将获得持续的下游改进。然而，这些趋势仅从单一模型提供商的孤立视角出发，而现实中提供商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规模扩展趋势的行为，甚至导致跨用户的整体预测准确性随规模扩展呈现非单调或下降趋势。我们定义了分类任务中的竞争模型，并利用数据表示作为研究规模扩展影响的透镜。研究发现，在许多场景中，提升数据表示质量（以贝叶斯风险衡量）反而会降低竞争性模型提供商市场中用户的整体预测准确性（即社会福利）。我们的示例涵盖简单场景下的闭式推导，以及在CIFAR-10上使用预训练表示进行的模拟仿真。在概念层面，本研究揭示：个体模型提供商有利的规模扩展趋势，在多提供商市场竞争环境中未必转化为社会福利的下游提升。