How to Fine-Tune a Reasoning Model? A Teacher-Student Cooperation Framework to Synthesize Student-Consistent SFT Data
Zixian Huang; Kaichen Yang; Xu Huang; Feiyang Hao; Qiming Ge; Bowen Li; He Du; Kai Chen · 2026 · arXiv
WASTE classifies this as Failed Experiment Report · AI classification, approximate
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Abstract (excerpt)
A widely adopted strategy for model enhancement is to use synthetic data generated by a stronger model for supervised fine-tuning (SFT). However, for emerging reasoning models like Qwen3-8B, this approach often fails to improve reasoning capabilities and can even lead to a substantial drop in performance. In this work, we identify substantial stylistic divergence between teacher generated data and the distribution of student as a major factor impacting SFT. To bridge this gap, we propose a Teacher-Student Cooperation Data Synthesis framework (TESSY), which interleaves teacher and student model
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Metadata source: arXiv