A blacksmith shop trains a tiny network on quench stills plus temperature notes, and both the training loss and the held-out loss stay high. What does that pattern indicate?

In a blacksmith shop’s multimodal quench-still and temperature-note training, high loss on both the training set and a held-out set indicates underfitting: the model never captured the underlying pattern on either split. Capacity may be too small, features poorly prepared, training too short, or the learning rate ineffective. Overfitting is the opposite silhouette—very low training loss with high holdout loss. Declaring dual-high loss “successful convergence” misreads the goal of optimization. A data-loading bug that appears only after perfect training fit does not describe the dual-high pattern; if both splits stay poor from the start, the model simply did not learn. Exam caveat: associate items test recognition of underfit versus overfit curves, not kernel debugging. Operational check: plot train and holdout loss together for the civic forge run; if both remain high and flat, increase capacity, fix feature readiness, lengthen training, or tune the learning rate before chasing regularization meant for overfitting. Before blaming labels, confirm features are network-ready and that the tiny network has enough capacity for quench patterns paired with temperature notes. Escalate capacity or feature readiness only after confirming labels and tensors for quench stills and temperature notes are correctly joined. Dual-high loss on quench stills and notes should trigger capacity and data-readiness review before any regularization meant for memorization. If both curves stay high, underfitting remains the working diagnosis until proven otherwise.