想搞懂思考模式和非思考模式怎么配比训练？这篇论文用数学题做了系统实验，告诉你数据比例和调度怎么影响推理准确率，还开源了基准。
本文系统研究了思维模式融合（TMF）训练中思考与非思考模式的数据比例和训练调度对数学推理的影响。作者构建了包含多种数据比例和三种训练调度的基准测试，发现增加非思考监督比例会降低思考模式准确率，两者存在不对称交互。不同训练调度可调节这种权衡，最优调度取决于数据比例。研究还量化了两种模式监督间的负相关，为TMF训练设计提供实践指导。代码和数据已在Fusion Bench开源。
Fusion Training for Mathematical Generalization in Large Language Models
Thinking Mode Fusion (TMF) enables large language models to support both concise responses and long-form reasoning by unifying a non-thinking mode and a thinking mode within a single model. However, its training dynamics, including the \emph{data ratio} and \emph{training schedule} between the two modes, remain underexplored. In this work, we present a systematic study of TMF by analyzing the effects of the training schedule and data ratio between thinking and non-thinking modes. Focusing on mathematical problem solving, we construct a benchmark with multiple thinking-to-non-thinking data ratios and three training schedules. Our results reveal an asymmetric interaction between the two modes: increasing the ratio of non-thinking supervision reduces the accuracy of the thinking mode. We further show that different training schedules modulate this trade-off and that the optimal schedule depends on the data ratio. Finally, we quantify a negative correlation between non-thinking and thinking mode supervision, highlighting an inherent tension between these two modes. These findings provide practical guidance for designing effective TMF training settings. All code and data are released to support further research at: \href{https://github.com/caocongfeng/Fusion-Bench.git}{\textbf{Fusion Bench}}.