QFFT, Question-Free Fine-Tuning for Adaptive Reasoning
Authors: name: Wanlong Liu, name: Junxiao Xu, name: Fei Yu, name: Yukang Lin, name: Ke Ji, name: Wenyu Chen, name: Yan Xu, name: Yasheng Wang, name: Lifeng Shang, name: 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 questions. 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
struggle. To enable models to leverage both patterns, we propose Question-Free
Fine-Tuning (QFFT), a fine-tuning approach that removes the input question
during training and learns exclusively from Long CoT responses. This approach
enables 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 exhibits
superior performance compared to SFT in noisy, out-of-domain, and low-resource
scenarios.