I’m dealing with a dataset that consists of 100 images with target values around (0-10), when I train my ML model on this I get proper convergence to an RMSE of 0.001. However, when I need to retrain this model and include a few data points orders of magnitude larger(1k-10k), I get, unsurprisingly, significant large losses that start around an RMSE of 300+ and decay to at most 5. Does anyone have any thoughts/approaches to dealing with regression models that contain target data that vary on orders of magnitude? Thanks!