Improve dataset card: Add paper/code links, task categories, and usage (#2)

- Improve dataset card: Add paper/code links, task categories, and usage (68be7676e0712d0526cd0561106991c56e21a4f1)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,4 +1,18 @@
 ---
+task_categories:
+- TEXT_GENERATION
+language:
+- en
+tags:
+- supervised-fine-tuning
+- reinforcement-learning
+- sokoban
+- general-points
+- chain-of-thought
+- instruction-following
+- reasoning
+- decision-making
+- llm
 dataset_info:
   features:
   - name: data_source
@@ -47,3 +61,98 @@ configs:
   - split: test_env
     path: data/test_env-*
 ---
+
+# Debunk the Myth of SFT Generalization Dataset
+
+This dataset is associated with the paper [Debunk the Myth of SFT Generalization](https://huggingface.co/papers/2510.00237).
+
+The research presented in the paper challenges the common belief that supervised fine-tuning (SFT) primarily memorizes training data and lacks generalization, especially when compared to reinforcement learning (RL). Through systematic evaluation on decision-making benchmarks like Sokoban and General Points, the authors demonstrate that incorporating prompt diversity and Chain-of-Thought (CoT) supervision during SFT training can lead to strong generalization across unseen instruction variants and strictly harder tasks, often matching or surpassing RL baselines.
+
+**Code:** [https://github.com/XiaofengLin7/debunking-sft-generalization](https://github.com/XiaofengLin7/debunking-sft-generalization)
+
+## Installation
+
+This dataset is intended for use with the associated code repository. To set up the environment and dependencies:
+
+### Prerequisites
+CUDA 12.2 & cuDNN 9.1.0 works, but [official docs](https://verl.readthedocs.io/en/latest/start/install.html) recommends CUDA >= 12.4 & cuDNN >= 9.8.0.
+
+### Setup
+
+```bash
+conda create -n debunk_sft python=3.10
+conda activate debunk_sft
+USE_MEGATRON=0 bash setup.sh
+git submodule init
+git submodule update
+pip install -e thirdparty/verl --no-dependencies
+pip install -e thirdparty/ragen --no-dependencies
+pip install -e thirdparty/alfworld --no-dependencies
+pip install -e thirdparty/trl --no-dependecies
+```
+
+## Dataset Overview
+
+This repository contains various datasets used in the research, categorized by task, method, diversity, and format. These datasets are part of a larger collection.
+
+| Task | Method | Diversity | Format | Link |
+| --- | --- | --- | --- | --- |
+| Sokoban | RL | non-diverse | — | [🤗](https://huggingface.co/datasets/Xiaofeng77/sokoban) |
+| Sokoban | RL | diverse | — | [🤗](https://huggingface.co/datasets/Xiaofeng77/diverse_sokoban) |
+| Sokoban | SFT | non-diverse | answer-only | [🤗](https://huggingface.co/datasets/Xiaofeng77/answer-only-sokoban) |
+| Sokoban | SFT | diverse | answer-only | [🤗](https://huggingface.co/datasets/Xiaofeng77/diverse-answer-only-sokoban) |
+| Sokoban | SFT | non-diverse | cot | [🤗](https://huggingface.co/datasets/Xiaofeng77/cot-sokoban) |
+| Sokoban | SFT | diverse | cot | [🤗](https://huggingface.co/datasets/Xiaofeng77/diverse-cot-sokoban) |
+| General Points | RL | non-diverse | — | [🤗](https://huggingface.co/datasets/Xiaofeng77/gp-l-only-10k) |
+| General Points | RL | diverse | — | [🤗](https://huggingface.co/datasets/Xiaofeng77/diverse-gp-l-only-10k) |
+| General Points | SFT | non-diverse | answer-only | [🤗](https://huggingface.co/datasets/Xiaofeng77/answer-only-gp-l-only-10k) |
+| General Points | SFT | diverse | answer-only | [🤗](https://huggingface.co/datasets/Xiaofeng77/diverse-answer-only-gp-l-only-10k) |
+| General Points | SFT | non-diverse | cot | [🤗](https://huggingface.co/datasets/Xiaofeng77/cot-gp-l-only-10k) |
+| General Points | SFT | diverse | cot | [🤗](https://huggingface.co/datasets/Xiaofeng77/diverse-cot-gp-l-only-10k) |
+
+## Sample Usage
+
+The following snippets from the GitHub repository demonstrate how to train models using this dataset with SFT (Supervised Fine-Tuning) or GRPO (Generative Reinforcement Policy Optimization).
+
+### Train your model with SFT
+Specify your model and data beforehand.
+
+For Sokoban:
+```bash
+bash debunk_sft/scripts/sokoban/sokoban_train_and_eval.sh
+```
+
+For General Points:
+```bash
+bash debunk_sft/scripts/gp_l/gp_l_train_and_eval.sh
+```
+
+### Train your model with GRPO
+
+Specify your model and data beforehand.
+
+For Sokoban:
+```bash
+bash debunk_sft/scripts/sokoban/sokoban_grpo.sh
+```
+
+For General Points:
+```bash
+bash debunk_sft/scripts/gp_l/gp_l_grpo.sh
+```
+
+## Citation
+
+If you use this dataset in your research, please cite the associated paper:
+
+```bibtex
+@misc{lin2024debunkthemythofsftgeneralization,
+      title={Debunk the Myth of SFT Generalization},
+      author={Xiaofeng Lin and Yuandong Tian and Huazhe Xu},
+      year={2024},
+      eprint={2510.00237},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2510.00237},
+}
+```