DoubtLess: Training-Free Contrastive Decoding for Efficient Reasoning

Key points

• Reasoning models achieve strong performance through long chains of thought (CoT), but often spend more tokens than needed to solve a problem.1
• Existing ways to shorten the CoT require additional training for each model, add a fixed steering vector to the model's hidden states, or use decoding heuristics such as stopping early or banning words like "wait", which can reduce accuracy.1
• On seven benchmarks of competition mathematics, graduate-level science and competitive programming, DoubtLess shortens the CoT of Qwen3-32B by 31% with unchanged accuracy, outperforming five inference-time baselines.1
• We also show that DoubtLess reduces thinking tokens per turn by 20% in a self-debugging coding agent with unchanged task performance.1

Sources (2)

• [1]DoubtLess: Training-Free Contrastive Decoding for Efficient Reasoning
  Hugging Face Daily Papers · Oct 13, 12:00 AM

  We propose DoubtLess, a training-free decoding method in which a much smaller reasoning model steers the large one towards brevity.

  Reasoning models achieve strong performance through long chains of thought (CoT), but often spend more tokens than needed to solve a problem.

• [2]DoubtLess: Training-Free Contrastive Decoding for Efficient Reasoning
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 05:59 PM · same content

Extractive summary: sentences quoted from the sources.