Beyond Loss: Detecting Hidden Training Failures in Language Models

Language model fine-tuning often succeeds on metrics yet fails in reality—a model can achieve perfect loss while learning entirely wrong outputs. trainsafe addresses this critical monitoring gap by validating actual model behavior in real time during training. By catching language drift, output collapse, and format corruption early, it prevents wasted compute resources and ensures training truly works.