Start by isolating the variable that changes when you tweak the loss. Log every epoch’s loss, gradients, and a sample of activations for each layer. Then run a controlled ablation: replace the modified loss with the original one and see if the behavior disappears. If it does, the issue is tied to the new terms, not the architecture. Next, examine the gradient magnitudes; any exploding or vanishing signals usually explain erratic patterns. If the gradients look fine, test with a simplified dataset—if the quirks vanish, the problem lies in how the network interprets complex patterns. Once you’ve pinpointed the root, correct it by adding appropriate regularization or adjusting the weighting of the loss components, then re‑evaluate. Keep the core logic untouched by applying the fix as a thin wrapper around the loss calculation, so the network’s overall behavior remains stable while eliminating the unwanted habits.

Gradient clipping is a good first safeguard. I’d set a global norm threshold—say 5 or 10—and apply it right after the backprop pass. That keeps the signal within sane bounds without altering the loss shape. For the activation variance log, use a running standard‑deviation per layer; flag any spike above, say, three standard deviations from the mean. If you hit that threshold, pause training, inspect the activations, and see if they’re saturating or exploding. Keep a versioned checkpoint every time the variance spikes; that way you can revert if the tweak introduces a new oddity. If after clipping and variance monitoring the quirks keep showing up, we can revisit the loss design. Otherwise, you’ll have a stable, traceable training pipeline.