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---
license: mit
datasets:
- Nickyang/ConciseR-Data
language:
- en
metrics:
- accuracy
base_model:
- Qwen/Qwen2.5-Math-7B
pipeline_tag: text-generation
---
<div align='center'>
<h2>Walk Before You Run! <br/>Concise LLM Reasoning via Reinforcement Learning</h2>
<!-- TODO: Paper, Models-->
[](https://arxiv.org/abs/2505.21178)
<a href="https://huggingface.co/collections/Nickyang/conciser-6827718942b90a6390db50c1" target="_blank"><img alt="Hugging Face"
src="https://img.shields.io/badge/HuggingFace-fcd022?style=for-the-badge&logo=huggingface&logoColor=000&labelColor"/></a>
</div>
## 🎉News
- **[2025/05/27]** 🎉 We release [**ConciseR-Zero-7B**](https://huggingface.co/Nickyang/ConciseR-Zero-7B) and [**ConciseR-Zero-7B-Preview**](https://huggingface.co/Nickyang/ConciseR-Zero-7B-Preview).
## Usage
```python
import vllm
def apply_template(question: str):
return ("""A conversation between User and Assistant. The User asks a question, and the Assistant solves it. \
The Assistant first thinks about the reasoning process in the mind and then provides the User with the answer. \
The reasoning process is enclosed within <think> </think> and answer is enclosed within <answer> </answer> tags, respectively, \
i.e., <think> reasoning process here </think> <answer> answer here </answer>. \
Please reason step by step, and put your final answer within \\boxed{}.
User:
{query}
Assistant:
""".replace("{query}", question))
model_name = "Nickyang/ConciseR-Zero-7B-Preview"
sampling_params = vllm.SamplingParams(
n=32,
temperature=0.6,
top_p=1.0,
max_tokens=3072,
)
model = vllm.LLM(
model_name,
max_model_len=4096,
dtype="bfloat16",
enable_prefix_caching=True,
)
prompts = [
"How many positive whole-number divisors does 196 have?"
]
prompts = list(map(apply_template, prompts))
outputs = model.generate(prompts, sampling_params)
print(outputs)
```
## Citation
```latex
@misc{song2025conciser,
title={Walk Before You Run! Concise LLM Reasoning via Reinforcement Learning},
author={Mingyang Song and Mao Zheng},
year={2025},
eprint={2505.21178},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.21178},
}
```
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