README.md

---
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-->
[![Paper](https://img.shields.io/badge/paper-5f16a8?style=for-the-badge&logo=arxiv&logoColor=white)](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 ("""<|startoftext|>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}, 
}
```