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README.md

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+---
+license: mit
+language:
+- en
+tags:
+- reinforcement-learning
+- agents
+- embodied-ai
+- instruction-following
+- webshop
+- alfworld
+---
+
+# SkillRL Reproduction Data
+
+This public dataset bundles the local data needed to reproduce the SkillRL
+training and environment setup.
+
+## Contents
+
+- `SkillRL-SFT-Data/`: supervised fine-tuning instruction/output pairs for
+  ALFWorld, WebShop, and Search.
+- `alfworld/`: extracted ALFWorld task definitions, logic files, and detector
+  checkpoint.
+- `webshop/data/`: full and 1,000-product WebShop JSON datasets.
+- `webshop/search_engine/`: generated Pyserini/Lucene resources and indexes.
+
+The current WebShop search resources contain 100 documents in the `100`
+variant and 1,000 documents in the default, `1k`, and `100k` variants, matching
+the upstream setup script's default 1,000-product input.
+
+## Source project
+
+See [aiming-lab/SkillRL](https://github.com/aiming-lab/SkillRL) for training and
+evaluation code and the original licenses and citations of the bundled
+benchmarks.

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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
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+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
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+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text
+webshop_sft_data.json filter=lfs diff=lfs merge=lfs -text

SkillRL-SFT-Data/README.md

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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}
+}
+```