Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- Jasaxion/MathSmith-Hard-Problems
+language:
+- en
+base_model:
+- Qwen/Qwen3-8B
+tags:
+- verl
+---
+
+**MathSmith: Towards Extremely Hard Mathematical Reasoning by Forging Synthetic Problems with a Reinforced Policy**
+
+[![Paper](https://img.shields.io/badge/arXiv-2508.05592-b31b1b.svg)](https://arxiv.org/abs/2508.05592)
+[![License](https://img.shields.io/badge/License-Apache%202.0-green.svg)](LICENSE)
+[![Python](https://img.shields.io/badge/Python-3.10%2B-blue.svg)]()
+[![GitHub](https://img.shields.io/badge/-GitHub-181717?logo=github)](https://github.com/Jasaxion/MathSmith)
+
+
+## Overview
+
+The model generates <rationale>–<problem> pairs, where:
+- `<rationale>`: structured reasoning describing concept integration and difficulty design.  
+- `<problem>`: a single Olympiad-level mathematical question that admits a verifiable numeric or symbolic answer.
+
+Compared with **MathSmith-HC** (complexity + consistency reward), **MathSmith-Hard** removes the consistency term to emphasize *maximum reasoning depth and difficulty*.
+
+---
+
+## MathSmith Pipeline
+
+The MathSmith framework consists of four main stages:
+
+1. **Concept Collection**: Randomly sample concept–explanation pairs from [PlanetMath](https://planetmath.org/) to ensure data independence.
+
+2. **Supervised Fine-tuning (SFT)**: Train the model on collected concept–explanation pairs to establish foundational understanding.
+
+3. **Reinforcement Learning (RL)**: Optimize the model using GRPO with rewards based on:
+   - Structural validity
+   - Reasoning complexity  
+   - Answer consistency
+
+4. **Weakness-Focused Self-Improvement**: Iteratively identify and address model weaknesses by generating targeted problem variants.
+
+
+## Dependence
+- Transformers 4.52.4
+- Pytorch 2.7.0+cu126
+- Datasets 3.6.0
+- Tokenizers 0.21.1
+
+## Citation
+
+If you find this work useful, please cite:
+
+```bibtex
+@article{zhan2025mathsmith,
+  title={MathSmith: Towards Extremely Hard Mathematical Reasoning by Forging Synthetic Problems with a Reinforced Policy},
+  author={Zhan, Shaoxiong and Lai, Yanlin and Lu, Ziyu and Lin, Dahua and Yang, Ziqing and Tan, Fei},
+  journal={arXiv preprint arXiv:2508.05592},
+  year={2025}
+}
+```