Confidence Is Not Truth, But It Can Steer: When LLMs Learn When to Stop
Stop Every production LLM workflow eventually meets the same boring question: should the model answer now, think again, or throw away the current path and try something else? That question sounds less glamorous than “build a bigger model.” It is also closer to where real deployment costs live. Reasoning models can improve by sampling more answers, extending chains of thought, or running repeated critique-and-revision loops. The bill, naturally, arrives in tokens, latency, GPU capacity, and engineering patience. The last item is rarely benchmarked, perhaps because it would make too many papers look expensive. ...