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--- |
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license: apache-2.0 |
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language: |
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- en |
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metrics: |
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- accuracy |
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base_model: |
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- deepseek-ai/DeepSeek-R1-Distill-Qwen-7B |
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--- |
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# 🚀 GRPO-LEAD: Efficient Reasoning Enhancement for Mathematical Tasks |
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--- |
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## 📚 Overview |
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**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. |
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--- |
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## 📊 Performance Benchmarks |
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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. |
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| **Model** | **AIME24 Cons@32** | **AIME24 Pass@1** | **AIME24 Avg. Length** | **AIME25 Cons@32** | **AIME25 Pass@1** | **AIME25 Avg. Length** | |
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|---------------------|--------------------|-------------------|------------------------|--------------------|-------------------|------------------------| |
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| **DeepSeek-Distlled-14B** | 0.800 | 0.614 | 9182 | 0.633 | 0.429 | 10046 | |
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| **Light-R1-14B-DS** | 0.833 | 0.641 | 9571 | 0.767 | 0.505 | 10194 | |
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| **LEAD-14B (ours)** | **0.867** | **0.650** | **8267** | **0.767** | **0.539** | **8668** | |
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Our GRPO-LEAD model achieves superior consistency and higher accuracy, demonstrating significantly improved reasoning efficiency as evidenced by shorter average reasoning lengths. |
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--- |
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## ⚙️ Usage |
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To achieve the best performance in solving mathematical problems, simply use the following prompt format: |
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```python |
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[ |
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{ |
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"role": "user", |
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"content": question + "\nLet's think step by step and output the final answer within \\boxed{}." |
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} |
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] |
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``` |
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--- |
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## 📂 Code and Documentation |
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For complete details, codebase, and usage examples, please visit our GitHub repository: |
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[**📌 GitHub Repository**](https://github.com/aeroplanepaper/GRPO-LEAD) |
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--- |
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## 📦 Dataset: GRPO-LEAD-SFTData |
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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). |
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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. |
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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., |
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--- |
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## 📖 Citation |
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If you find our work useful, please cite it as: |
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```bibtex |
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@misc{zhang2025grpoleaddifficultyawarereinforcementlearning, |
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title={GRPO-LEAD: A Difficulty-Aware Reinforcement Learning Approach for Concise Mathematical Reasoning in Language Models}, |
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author={Jixiao Zhang and Chunsheng Zuo}, |
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year={2025}, |
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eprint={2504.09696}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2504.09696}, |
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} |
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``` |
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Enjoy exploring GRPO-LEAD! 🚀✨ |
