One-shot neural architecture search (NAS) is an efficient and widely adopted method for powerful networks. However, it suffers from inconsistency, where sub-networks inheriting supernet weights exhibit different performance rankings compared to networks trained independently from scratch. To address this issue, we present a theoretical analysis showing that the discrepancy in performance ranking between sub-networks and their optimal counterparts is strongly correlated with the gradient norm and weight variance of the supernet. Based on these insights, we propose two regularization techniques and introduce Oneshotimizer, a novel method designed to enhance the consistency and effectiveness of one-shot NAS. Experiments demonstrate that Oneshotimizer significantly improves both ranking consistency and search performance. Specifically, it achieves a 14.8% increase in Kendall’s \(\uptau \) over SPOS on NAS-Bench-201, and a state-of-the-art 77.8% Top-1 accuracy with only 395M FLOPs on ImageNet.