datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
answer-only-gp-l-only-10k
Debunk the Myth of SFT Generalization Dataset
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.diverse-answer-only-gp-l-only-10k
General Points Dataset from Debunk the Myth of SFT Generalization
This dataset is part of the research presented in the paper Debunk the Myth of SFT Generalization. It contains data for the General Points decision-making benchmark, which is used to evaluate the generalization capabilities of Supervised Fine-Tuning (SFT) models against Reinforcement Learning (RL) baselines. The paper explores the impact of prompt diversity and Chain-of-Thought (CoT) supervision on SFT's ability to… See the full description on the dataset page: https://huggingface.co/datasets/Xiaofeng77/diverse-answer-only-gp-l-only-10k.diverse-answer-only-sokoban
Dataset from "Debunk the Myth of SFT Generalization"
This dataset is associated with the research presented in the paper Debunk the Myth of SFT Generalization.
The paper challenges the conventional wisdom that supervised fine-tuning (SFT) primarily memorizes training data and struggles with generalization, contrasting it with reinforcement learning (RL)'s perceived robustness. Through systematic evaluation on decision-making benchmarks such as Sokoban and General Points, the… See the full description on the dataset page: https://huggingface.co/datasets/Xiaofeng77/diverse-answer-only-sokoban.