When training a linear regression model from scratch on the Airfoil Self-Noise dataset using the RMSProp optimizer with an initial learning rate of 0.01 and a decay parameter of $\gamma = 0.9$, the training loss converges to approximately 0.245. Setting $\gamma = 0.9$ means the algorithm aggregates, on average, over the past 10 observations ($\frac{1}{1-\gamma}$) of the squared gradient. This typical configuration uses a modest learning rate paired with a high decay factor, contrasting with AdaGrad which often demands a larger initial learning rate to counteract its aggressive learning rate decay.