Through regularization techniques, data augmentation, loss functions, and gradient optimization, ensuring the model focuses on meaningful patterns and reduces overfitting to noise.

It’s not obvious how any of those would do anything but better approximate the average of a noisy dataset. RLHF might help, but only if it’s not done by idiots.