How does gradient scaling prevent underflow in mixed precision training?

Gradient scaling multiplies loss to prevent small gradients from underflowing in FP16.

In FP16, small gradient values may become zero due to limited precision. GradScaler scales loss before backpropagation and unscales gradients before optimizer step to maintain numerical stability.