The researchers found a better way to choose the right model size in linear regression, especially when the data is noisy. Instead of just looking at all possible models, they also considered smaller models within each size. This new method improved accuracy, especially for low signal-to-noise ratios, across different model selection criteria. The performance of the new method was particularly noticeable for bootstrap-based criteria.