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".
Agent-STAR is a systematic study of the reinforcement learning (RL) design space for long-horizon tool-using agents using the 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
- Repository: GitHub - Agent-STAR
- Dataset: 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:
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 as described in the GitHub repository.
Citation
If you find Agent-STAR helpful to your work, please cite the following:
@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 for their benchmark and the rLLM framework contributors for supporting the RL training process.