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---
license: apache-2.0
language:
- en
metrics:
- accuracy
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
---
# 🚀 GRPO-LEAD: Efficient Reasoning Enhancement for Mathematical Tasks
---
## 📚 Overview
**GRPO-LEAD** (**GRPO** with **L**ength-dependent rewards, **E**xplicit penalties, and **A**dvantage reweighting for **D**ifficulty) is an advanced reinforcement learning pipeline designed to fine-tune large language models (LLMs) for concise, accurate, and efficient reasoning in mathematical tasks.
---
## 📊 Performance Benchmarks
The following benchmarks were conducted on AIME24 and AIME25 datasets, evaluated with parameters: 14k maximum tokens, temperature of 0.6, min-p of 0.01, and 32 samples per question.
| **Model** | **AIME24 Cons@32** | **AIME24 Pass@1** | **AIME24 Avg. Length** | **AIME25 Cons@32** | **AIME25 Pass@1** | **AIME25 Avg. Length** |
|---------------------|--------------------|-------------------|------------------------|--------------------|-------------------|------------------------|
| **DeepSeek-Distlled-14B** | 0.800 | 0.614 | 9182 | 0.633 | 0.429 | 10046 |
| **Light-R1-14B-DS** | 0.833 | 0.641 | 9571 | 0.767 | 0.505 | 10194 |
| **LEAD-14B (ours)** | **0.867** | **0.650** | **8267** | **0.767** | **0.539** | **8668** |
Our GRPO-LEAD model achieves superior consistency and higher accuracy, demonstrating significantly improved reasoning efficiency as evidenced by shorter average reasoning lengths.
---
## ⚙️ Usage
To achieve the best performance in solving mathematical problems, simply use the following prompt format:
```python
[
{
"role": "user",
"content": question + "\nLet's think step by step and output the final answer within \\boxed{}."
}
]
```
---
## 📂 Code and Documentation
For complete details, codebase, and usage examples, please visit our GitHub repository:
[**📌 GitHub Repository**](https://github.com/aeroplanepaper/GRPO-LEAD)
---
## 📦 Dataset: GRPO-LEAD-SFTData
We release [**GRPO-LEAD-SFTData**](https://huggingface.co/datasets/PlanePaper/GRPO-LEAD-SFTData), a curated collection of **12,153** high-quality mathematical reasoning samples for supervised fine-tuning. Generated via [**QwQ-32B**](https://huggingface.co/Qwen/QwQ-32B).
Derived primarily from the **DeepScaler** dataset ([DeepScaler](https://github.com/agentica-project/rllm)), we retain only examples with **difficulty > 1**, targeting challenging problem-solving scenarios. All entries are structured for seamless integration with [**LLaMA Factory**](https://github.com/hiyouga/LLaMA-Factory) and follow a standardized SFT-ready format.
Used as the training data for GRPO-LEAD’s supervised fine-tuning stage, this dataset is able to increase the model's base capability in solving mathematical problems.,
---
## 📖 Citation
If you find our work useful, please cite it as:
```bibtex
@misc{zhang2025grpoleaddifficultyawarereinforcementlearning,
title={GRPO-LEAD: A Difficulty-Aware Reinforcement Learning Approach for Concise Mathematical Reasoning in Language Models},
author={Jixiao Zhang and Chunsheng Zuo},
year={2025},
eprint={2504.09696},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2504.09696},
}
```
Enjoy exploring GRPO-LEAD! 🚀✨
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