In a pre-training framework where one neural network (the 'generator') replaces tokens in a text and a second network (the 'discriminator') identifies these replacements, both are trained simultaneously by combining their respective loss functions. Analyze the dynamic relationship between the generator's training objective (predicting original tokens) and the discriminator's training objective (classifying tokens). Specifically, explain how the improvement of the generator's performance creates a more challenging and effective learning task for the discriminator.