Learning What to Learn: Stage-Specific Data Sets for SFT-then-RL in Small Language Model Reasoning 事件

Learning What to Learn: Stage-Specific Data Sets for SFT-then-RL in Small Language Model Reasoning arXiv:2606.04466v1 Announce Type: new Abstract: Post-training Small Language Models (SLMs) for reasoning typically follows an SFT-then-RL pipeline, yet existing work rarely considers what data should be learned at each stage. We argue that data strategy should be aligned with the distinct roles of SFT and RL: SFT is better suited for acquiring not-yet-mastered reasoning skills, while RL is better