Minghao Luo

Reasoning models often keep re-checking after they have reached an answer, stretching the chain of thought. DoubtLess has a small model that shares the large model's tokenizer read the same chain twice, once told to go straight to the answer and once told to double-check every step, and adds the scaled difference of the two logit vectors to the large model's logits at every step, acting only at decoding time with no weight changes. On seven competition-mathematics, graduate-science and programming benchmarks, Qwen3-32B steered by Qwen3-0.6B uses 31% fewer thinking tokens at unchanged accuracy, a larger cut than five inference-time baselines.

Reasoning models often keep re-checking after they have reached an answer, stretching the chain of thought. DoubtLess has a small model that shares the large model's tokenizer read the same chain twice, once told to go straight to the answer and once told to double-check every step, and adds the scaled difference of the two logit vectors to the large model's logits at every step, acting only at decoding time with no weight changes. On seven competition-mathematics, graduate-science and programming benchmarks, Qwen3-32B steered by Qwen3-0.6B uses 31% fewer thinking tokens at unchanged accuracy, a larger cut than five inference-time baselines.