---
license: mit
task_categories:
- other
tags:
- reinforcement-learning
- tool-use
- llm-agents
---

# Agent-STAR TravelDataset

This repository contains the synthetic queries and datasets presented in the paper [Demystifying Reinforcement Learning for Long-Horizon Tool-Using Agents: A Comprehensive Recipe](https://huggingface.co/papers/2603.21972). 

The dataset serves as a long-horizon tool-use testbed for Reinforcement Learning (RL) research, specifically utilizing the TravelPlanner environment.

- **Paper:** [Demystifying Reinforcement Learning for Long-Horizon Tool-Using Agents: A Comprehensive Recipe](https://huggingface.co/papers/2603.21972)
- **GitHub Repository:** [WxxShirley/Agent-STAR](https://github.com/WxxShirley/Agent-STAR)

## Dataset Description

The dataset includes over **17K** synthetic queries generated through a three-step pipeline: element sampling, feasibility checking, and LLM back-translation. These queries require agents to iteratively call tools to satisfy multifaceted constraints.

### File Information

| Data | Description |
|---|---|
| `TravelPlanner_Val180.jsonl` | Official TravelPlanner validation set of 180 instances |
| `TravelTotal_17K.jsonl` | All 17K+ synthetic queries after element sampling, feasibility checking, and back-translation |
| `Travel_Mixed_1K_RL.jsonl` | Default 1K RL training set with mixed difficulty |
| `Travel_Easy_1K.jsonl` | Difficulty-specific 1K set (Easy) for controlled experiments |
| `Travel_Medium_1K.jsonl` | Difficulty-specific 1K set (Medium) for controlled experiments |
| `Travel_Hard_1K.jsonl` | Difficulty-specific 1K set (Hard) for controlled experiments |

## Related Resources

- **Travel Database:** [Agent-STAR-TravelDatabase](https://huggingface.co/datasets/xxwu/Agent-STAR-TravelDatabase) (Required for environment interactions)
- **Models:** [Agent-STAR Collection](https://huggingface.co/collections/xxwu/agent-star)

## Citation

```bibtex
@misc{wu2026agentstar,
      title={Demystifying Reinforcement Learning for Long-Horizon Tool-Using Agents: A Comprehensive Recipe}, 
      author={Xixi Wu and Qianguo Sun and Ruiyang Zhang and Chao Song and Junlong Wu and Yiyan Qi and Hong Cheng},
      year={2026},
      eprint={2603.21972},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2603.21972}, 
}
```