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 mod...
| Authors | Congfeng Cao, Pengyu Zhang, Jelke Bloem |
|---|---|
| Primary category | cs.CL |
| Published | 10 August 2026 |
| Last updated at source | 10 August 2026 |
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