Question-Free Fine-Tuning: Towards Efficient and Adaptive Reasoning in Large Language Models
Wanlong Liu, Junxiao Xu, Fei Yu, Yukang Lin, Ke Ji, Wenyu Chen, Lifeng Shang, Yasheng Wang, Yan Xu, Benyou Wang
Recent advancements in long chain-of-thought (CoT) reasoning models have improved performance on complex tasks, but they suffer from overthinking, which generates redundant reasoning steps, especially for simple problems. This paper revisits the reasoning patterns of long and short CoT models, observing that the short CoT patterns offer concise reasoning efficiently, while the long CoT patterns excel in challenging scenarios where the short CoT patterns struggles. To enable models to leverage both patterns, we propose Question-Free Fine-Tuning (QFFT), a distillation approach that removes the input question during training and learns exclusively from reasoning chains. This approach allows the model to adaptively employ both reasoning patterns: it prioritizes the short CoT patterns and activates the long CoT patterns only when necessary. Experiments on various mathematical datasets demonstrate that QFFT reduces average response length by more than 50\%, while achieving performance comparable to Supervised Fine-Tuning (SFT). Additionally, QFFT achieves superior generalization on out-of-domain tasks.