This dataset is associated with the paper "Debunk the Myth of SFT Generalization". The paper challenges the prevailing view that supervised fine-tuning (SFT) primarily memorizes training data and fails to generalize, in contrast to reinforcement learning (RL). It demonstrates that SFT can generalize as well as—or better than—RL when trained with appropriate data, achieved through prompt diversity and Chain-of-Thought (CoT) supervision on… See the full description on the dataset page:
https://huggingface.co/datasets/Xiaofeng77/answer-only-gp-l-only-10k.
