---
license: mit
library_name: transformers
pipeline_tag: text-generation
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- reinforcement-learning
- tool-use
- agent
- travel-planner
---

# Agent-STAR-RL-7B

Agent-STAR-RL-7B is a 7B parameter model based on **Qwen2.5-7B-Instruct**, fine-tuned using Reinforcement Learning (RL) for long-horizon tool-use tasks. 

This model is a key artifact of the paper [Demystifying Reinforcement Learning for Long-Horizon Tool-Using Agents: A Comprehensive Recipe](https://huggingface.co/papers/2603.21972).

## Model Description

The model was developed using the **STAR [Data Synthesis → SFT → RL]** pipeline, a unified post-training recipe for scaling RL in complex, multi-turn environments. It is specifically optimized for [TravelPlanner](https://github.com/OSU-NLP-Group/TravelPlanner/), a challenging testbed requiring tool orchestration to satisfy multifaceted commonsense and hard constraints. 

As per the systematic study in the paper, the 7B variant leverages **GRPO (Group Relative Policy Optimization)** with a dense **SUM reward** for optimized performance and faster convergence.

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

## Training Pipeline

1. **Data Synthesis:** Generation of synthetic queries and successful trajectories.
2. **SFT:** Fine-tuning from the backbone using ~1K successful trajectories.
3. **RL:** Scale-aware reinforcement learning tuning.

## Usage

This model is designed to be used within a ReAct-style agentic framework. For reproducing the results on TravelPlanner, it is recommended to use the inference code provided in the official repository.

### Inference Example
From the [Agent-STAR](https://github.com/WxxShirley/Agent-STAR) repository root:
```bash
cd Inference
python3 -u main.py \
  --model xxwu/Agent-STAR-RL-7B \
  --save_suffix test_run \
  --max_workers 20 \
  --split validation \
  --max_context 32768 \
  --max_turns 60 
```

## 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}, 
}
```