Puzzling Success of Overparameterization: Lottery Tickets or Escape Dimensions?

This research challenges the prevailing 'lottery ticket' explanation for why oversized AI models succeed, proposing instead that larger networks work because they provide more dimensional space to escape poor optimization outcomes—a distinction with significant implications for how organizations should approach model development, resource allocation, and infrastructure scaling. Rather than needing to search through subnetworks in parallel, overparameterized models leverage expanded geometric space to find better solutions more reliably, suggesting that IT leaders should reconsider their assumptions about neural network efficiency and the trade-offs between model size, training costs, and performance gains. Understanding this fundamental mechanism helps technology organizations make more informed decisions about GPU/computing resource investments, model architecture choices, and the actual efficiency costs of deploying larger AI systems in production.