Reasoning-Trace Collapse: Evaluating the Loss of Explicit Reasoning During Fine-Tuning
Standard fine-tuning can cause models to rapidly lose their ability to produce complete reasoning steps, even if they still get the final answer right. This 'reasoning-trace collapse' happens when models learn to skip detailed thinking during adaptation because training data lacks explicit reasoning traces. The study shows that standard metrics hide this failure, necessitating new structural metrics.