<ul><li>Neural scaling laws indicate that the test error of large language models decreases as model size and data size increase.</li><li>Data reuse can enhance scaling laws in linear regression by improving test error bounds on models trained using multi-pass stochastic gradient descent.</li><li>The study shows that with data reuse, multi-pass SGD achieves a better test error compared to one-pass SGD in certain data-constrained scenarios.</li><li>Numerical simulations validate the theoretical results presented in the research work.</li></ul>

Improved Scaling Laws in Linear Regression via Data Reuse

Discover more