Why does a Keras model suddenly diverge after several epochs of stable training?

Training divergence is often caused by unstable learning rates, gradient explosion, or data distribution shifts during training.

Even if early training is stable, later divergence can occur due to learning rate schedules increasing too aggressively, unstable batch normalization statistics, or corrupted batches. In distributed setups, desynchronization between replicas can also trigger divergence.