Your best guide is the pair of curves: training loss and validation loss over steps. Read them together. They tell you which failure mode you are in — and when to stop.
Intuition
Pattern
What it means
What to try
Underfitting
Both losses stay high and plateau early
Train longer, improve data, or give the model more room to learn
Overfitting
Train loss keeps falling while validation loss turns back up
Stop earlier, simplify updates (freeze / smaller learning rate), add better or more diverse data
No learning
Both curves flat near the starting value
Check data pipeline, labels, learning rate, and that gradients are actually flowing
Healthy fine-tune
Both losses fall together with a small, stable gap
Stop near the validation-loss minimum
How it works
What good learning looks like
Hallmarks of a healthy run:
Curves move together — Validation follows training downward; the model is generalizing, not only memorizing.
Small, stable gap — A modest train–val gap that stops widening is normal.
Diminishing returns — Loss flattens smoothly; you do not see wild oscillation, spikes, or divergence.
Stop at the val minimum — Checkpoint there. Training longer often buys nothing (or buys overfitting).
Tie-back to earlier chapters
Spikes and divergence → revisit the training instability checklist.
Task up, general skills down → revisit catastrophic forgetting canaries (loss curves alone will not show that).
Curves look sick despite a sane schedule → revisit data preparation.
What goes wrong
Shipping the checkpoint with the lowest training loss.
Ignoring validation until the end of a long run.
Reading only one curve and calling it “done.”
One-line summary
Watch training and validation loss together: fall together with a small gap means healthy learning; stop at the validation minimum.
Key terms
Underfitting — Model never gets good on train or validation.