Fusion Training for Mathematical Generalization in Large Language Models
Score
77首次系统量化TMF训练中思维与非思维模式的负相关关系，填补了该方向空白。
实验设计严谨，构建多比例多调度基准，结论清晰且可复现。
为工程实践中TMF训练的数据配比与调度选择提供了直接可用的指导。
思维融合是当前大模型推理优化的前沿热点，该成果具有及时参考价值。
来自arXiv AI/CL/LG预印本，并开放代码与数据，公开透明可信。
Base 85 × T2 Source weight ×0.9 = 77
Curation line 70Selected
Why selected
This paper systematically dissects the core trade-off in TMF training, quantifying for the first time the negative correlation between non-thinking and thinking supervision, offering both theoretical and practical value.
AI commentary
「This paper is especially valuable for those training reasoning models—it quantifies the cost of switching between thinking modes rather than staying conceptual. More non-thinking data degrades deep reasoning, a tension rarely demonstrated with data before. Notably, they open-sourced Fusion Bench, which will make future comparisons of scheduling strategies much easier.」
Quick overview
This paper systematically studies Thinking Mode Fusion (TMF) training, which enables a single large language model to support both concise responses and long-form reasoning. The authors analyze the effects of data ratio and training schedule between thinking and non-thinking modes on mathematical problem solving, revealing an asymmetric interaction: increasing non-thinking supervision reduces thinking-mode accuracy. Different training schedules modulate this trade-off, and the optimal schedule depends on the data ratio. The study quantifies a negative correlation between the two modes, highlighting inherent tension and offering practical guidance for effective TMF training.
- Proposes a fusion training framework uniting non-thinking and thinking modes in one model
- Finds asymmetric interaction and negative correlation between thinking and non-thinking supervision
- Shows optimal training schedule depends on data ratio, guiding TMF configuration
- Releases code and data (Fusion Bench) to support further research
Full text
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