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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}
}
``` |