Propose an architectural change or algorithmic improvement to your system’s cross-entropy loss computation to prevent numerical instability and divergence.

Introduce a small smoothing factor (epsilon) to avoid encountering expressions like 0 log 0, which cause underflow due to catastrophic cancellation. This tradeoff comes at the cost of slightly higher training time and model capacity, but ensures stable and accurate computation of cross-entropy losses.