让模型自适应选择简繁推理路径，减少冗余输出。
QFFT, Question-Free Fine-Tuning for Adaptive Reasoning
- 训练时不输入问题，只学长链推理模式
- 响应长度减少超50%，性能接近监督微调
- 在噪声、低资源等场景下表现更优
近期长链思维（Long CoT）推理模型虽提升了复杂任务表现，但存在过度思考问题，尤其对简单问题生成冗余推理步骤。本文分析长短链推理模式发现：短链模式高效简洁，长链模式在难题中更优。为此提出无问题微调（QFFT），训练时移除输入问题，仅从长链推理结果中学习。该方法使模型能自适应选择推理路径：简单问题用短链，复杂问题激活长链。在多个数学数据集上的实验表明，QFFT将平均响应长度减少超50%，性能与监督微调（SFT）相当；且在噪声、跨域和低资源场景下优于SFT。
原文摘要 · Abstract (English)
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.
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