pzanna/OWL-SFT

---
license: apache-2.0
task_categories:
- question-answering
language:
- en
size_categories:
- 1K<n<10K
---
# OWL SFT (Planner) Dataset

## Dataset Summary

**OWL SFT** is a supervised fine‑tuning dataset designed for training the *planner* agent in the Optimized Workforce Learning (OWL) framework – a system for multi‑agent assistance in real‑world task automation.
The dataset contains **1,564** multi‑turn conversations, focusing on **task decomposition, sequencing, and coordination** skills that are crucial for high‑level planning.

## Languages

All conversation turns are written in **English**.

## Dataset Structure

### Data Fields

| Column | Type | Description |
| --------------- | ------------- | ------------------------------------------------------------------------------------------- |
| `task_id` | *string* | Unique identifier for each task instance. |
| `question` | *string* | Original user query or high‑level task goal. |
| `conversations` | *list\[dict]* | Ordered list of dialogue turns, each with `role` (`user` / `assistant`) and `content` keys. |

### Data Splits

Only a single **train** split is provided. Users may create validation/test splits via random sampling or k‑fold cross‑validation as required.

### Data Instances

```json
{
"task_id": "3b78e7c6",
"question": "Plan a weekend trip to Kyoto for two people.",
"conversations": [
{"role": "user", "content": "I want to spend a weekend in Kyoto with my partner."},
{"role": "assistant", "content": "Sure! Let me break this down into travel, accommodation, food, and activities..." }
]
}
```

#### Personal & Sensitive Information

The dataset does **not** contain personally identifiable information. All examples are synthetic or anonymised.

## Additional Information

### Citation

```bibtex
@article{hu2025owl,
title={Owl: Optimized workforce learning for general multi-agent assistance in real-world task automation},
author={Hu, Mengkang and Zhou, Yuhang and Fan, Wendong and Nie, Yuzhou and Xia, Bowei and Sun, Tao and Ye, Ziyu and Jin, Zhaoxuan and Li, Yingru and Chen, Qiguang and others},
journal={arXiv preprint arXiv:2505.23885},
year={2025}
}
```