Update README.md

README.md

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----
-license: apache-2.0
----
+---
+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! 🚀✨