| --- |
| dataset_info: |
| - config_name: alfworld |
| features: |
| - name: instruction |
| dtype: string |
| - name: output |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 42191393 |
| num_examples: 7486 |
| download_size: 5617790 |
| dataset_size: 42191393 |
| - config_name: search |
| features: |
| - name: instruction |
| dtype: string |
| - name: output |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 6659585 |
| num_examples: 1214 |
| download_size: 1050795 |
| dataset_size: 6659585 |
| - config_name: webshop |
| features: |
| - name: instruction |
| dtype: string |
| - name: output |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 12351072 |
| num_examples: 2553 |
| download_size: 1652500 |
| dataset_size: 12351072 |
| configs: |
| - config_name: alfworld |
| data_files: |
| - split: train |
| path: alfworld/train-* |
| - config_name: search |
| data_files: |
| - split: train |
| path: search/train-* |
| - config_name: webshop |
| data_files: |
| - split: train |
| path: webshop/train-* |
| license: mit |
| language: |
| - en |
| tags: |
| - reinforcement-learning |
| - embodied-ai |
| - instruction-following |
| - SFT |
| - agent |
| size_categories: |
| - 10K<n<100K |
|
|
| --- |
| |
| # SkillRL-SFT-Data |
|
|
| This is the **Supervised Fine-Tuning (SFT) dataset** used in the paper [SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning](https://arxiv.org/abs/2602.08234). |
|
|
| SkillRL-SFT-Data provides instruction-output pairs for training base agent policies on three interactive decision-making environments: **ALFWorld**, **WebShop**, and **Search**. Each example contains a structured instruction with retrieved skill context from the hierarchical SkillBank and the corresponding expert action output. |
|
|
| ## Dataset Summary |
|
|
| | Config | Environment | Examples | Description | |
| | ---------- | --------------------------------------------------- | -------- | ------------------------------------------------------------ | |
| | `alfworld` | [ALFWorld](https://github.com/alfworld/alfworld) | 7,486 | Embodied household tasks (pick & place, clean, heat, cool, examine, etc.) | |
| | `webshop` | [WebShop](https://github.com/princeton-nlp/WebShop) | 2,553 | Web-based shopping navigation tasks | |
| | `search` | Search | 1,214 | Multi-step web search QA tasks (NQ, TriviaQA, PopQA, HotpotQA, 2Wiki, MuSiQue, Bamboogle) | |
|
|
| **Total**: 11,253 instruction-output pairs. |
|
|
| ## Data Format |
|
|
| Each example contains two fields: |
|
|
| - **`instruction`**: A detailed prompt including the task goal, retrieved relevant experience from the hierarchical SkillBank (general principles, task-specific skills, and mistakes to avoid), current environment observation, and admissible actions. |
| - **`output`**: The expected agent response, consisting of a step-by-step reasoning process wrapped in `<think>` tags followed by the selected action in `<action>` tags. |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Load a specific config |
| alfworld_data = load_dataset("Jianwen/SkillRL-SFT-Data", "alfworld") |
| webshop_data = load_dataset("Jianwen/SkillRL-SFT-Data", "webshop") |
| search_data = load_dataset("Jianwen/SkillRL-SFT-Data", "search") |
| ``` |
|
|
| ## Related Models |
|
|
| | Environment | SFT Checkpoint | RL Checkpoint | |
| | ----------- | ------------------------------------------------------------ | ------------------------------------------------------------ | |
| | ALFWorld | [Alfworld-7B-SFT](https://huggingface.co/Jianwen/Alfworld-7B-SFT) | [Alfworld-7B-RL](https://huggingface.co/Jianwen/Alfworld-7B-RL) | |
| | WebShop | [Webshop-7B-SFT](https://huggingface.co/Jianwen/Webshop-7B-SFT) | [Webshop-7B-RL](https://huggingface.co/Jianwen/Webshop-7B-RL) | |
| | Search | [Search-7B-SFT](https://huggingface.co/Jianwen/Search-7B-SFT) | [Search-7B-RL](https://huggingface.co/Jianwen/Search-7B-RL) | |
|
|
| All models are fine-tuned from [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct). SFT checkpoints are trained on this dataset; RL checkpoints are further optimized via recursive skill-augmented reinforcement learning. |
|
|
| ## Related Resources |
|
|
| - **Paper**: [SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning](https://arxiv.org/abs/2602.08234) |
| - **Code**: [https://github.com/aiming-lab/SkillRL](https://github.com/aiming-lab/SkillRL) |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{xia2026skillrl, |
| title={SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning}, |
| author={Xia, Peng and Chen, Jianwen and Wang, Hanyang and Liu, Jiaqi and Zeng, Kaide and Wang, Yu and Han, Siwei and Zhou, Yiyang and Zhao, Xujiang and Chen, Haifeng and others}, |
| journal={arXiv preprint arXiv:2602.08234}, |
| year={2026} |
| } |
| ``` |
