Add model card and paper link (#1)

- Add model card and paper link (a169c5afe03f19402e5b97546a63beb6a153bcac)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,3 +1,64 @@
----
-license: mit
----
+---
+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}, 
+}
+```