| --- |
| 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}, |
| } |
| ``` |
