---
license: mit
library_name: transformers
pipeline_tag: text-generation
base_model: Qwen/Qwen2.5-1.5B-Instruct
tags:
- tool-use
- reinforcement-learning
- agent
- travel-planning
---

# Agent-STAR-RL-1.5B

This repository contains the **Agent-STAR-RL-1.5B** model, which is part of the research presented in the paper "[Demystifying Reinforcement Learning for Long-Horizon Tool-Using Agents: A Comprehensive Recipe](https://huggingface.co/papers/2603.21972)".

Agent-STAR is a systematic study of the reinforcement learning (RL) design space for long-horizon tool-using agents using the [TravelPlanner](https://github.com/OSU-NLP-Group/TravelPlanner/) testbed. The model is trained using the **STAR** pipeline: **Data Synthesis → SFT → RL**.

## Model Details

- **Backbone:** Qwen2.5-1.5B-Instruct
- **Training Stage:** Reinforcement Learning (RL)
- **Primary Task:** Long-horizon tool orchestration and planning.
- **Paper:** [Demystifying Reinforcement Learning for Long-Horizon Tool-Using Agents: A Comprehensive Recipe](https://huggingface.co/papers/2603.21972)
- **Repository:** [GitHub - Agent-STAR](https://github.com/WxxShirley/Agent-STAR)
- **Dataset:** [Agent-STAR-TravelDataset](https://huggingface.co/datasets/xxwu/Agent-STAR-TravelDataset)

According to the paper's findings, smaller models like this 1.5B variant benefit from scale-aware recipes including staged (curriculum-style) rewards and enhanced exploration to handle the complex constraints of multi-turn environments.

## Usage

To run ReAct inference using the official implementation, you can use the following command structure:

```shell
cd Inference
python3 -u main.py \
  --model xxwu/Agent-STAR-RL-1.5B \
  --save_suffix your_suffix \
  --max_workers 20 \
  --split validation \
  --max_context 32768 \
  --max_turns 60 
```

Note: You will need to prepare the [travel database](https://huggingface.co/datasets/xxwu/Agent-STAR-TravelDatabase) as described in the GitHub repository.

## Citation

If you find Agent-STAR helpful to your work, please cite the following:

```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}, 
}
```

## Acknowledgements

We thank the authors of [TravelPlanner](https://github.com/OSU-NLP-Group/TravelPlanner/) for their benchmark and the [rLLM](https://github.com/rllm-org/rllm/) framework contributors for supporting the RL training process.