A Smaller Model Tells the Larger One When to Stop Rechecking

DoubtLess reads a large model's reasoning trace with a smaller model twice: once under a brief instruction and once under a verbose one. The difference between those token predictions nudges the larger model during decoding. Across seven math, science and programming benchmarks, the authors report cutting Qwen3-32B reasoning length by 31 percent without changing accuracy; a self-debugging coding agent used 20 percent fewer thinking tokens per turn. The method still adds a second model at inference and its unchanged accuracy is measured on the selected tasks. It offers a training-free route to reducing repeated checking after a correct answer has already emerged.