# Math Reasoning Arena: End-to-End Training Practice for Lightweight Math Reasoning Models

> A complete two-stage alignment project that transforms a 0.5B-parameter base model into a professional math reasoning assistant using SFT (Supervised Fine-Tuning) and DPO (Direct Preference Optimization) techniques, supporting CPU training and featuring an interactive web interface.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-06-07T16:15:49.000Z
- 最近活动: 2026-06-07T16:19:48.334Z
- 热度: 141.9
- 关键词: LLM, 数学推理, DPO, SFT, 模型微调, Qwen, 轻量级模型, CPU训练
- 页面链接: https://www.zingnex.cn/en/forum/thread/math-reasoning-arena
- Canonical: https://www.zingnex.cn/forum/thread/math-reasoning-arena
- Markdown 来源: floors_fallback

---

## Introduction to Math Reasoning Arena: End-to-End Training Project for Lightweight Math Reasoning Models

**Core Points**: Math Reasoning Arena is a complete two-stage alignment project that transforms a 0.5B-parameter base model into a professional math reasoning assistant using SFT (Supervised Fine-Tuning) and DPO (Direct Preference Optimization) techniques, supporting CPU training and featuring an interactive web interface.

**Project Basic Information**:
- Original Author/Maintainer: mostafanasr300
- Source Platform: GitHub
- Original Link: https://github.com/mostafanasr300/math-reasoning-dpo
- Release Time: June 2026

This project aims to lower the barrier to training math reasoning models, enabling individual developers and small teams to participate.

## Project Background and Motivation

Math reasoning is a weak point of large language models; even large-parameter models often make logical errors. Traditional training to improve math ability requires significant computing resources, which deters individual developers.

This project proves that through a well-designed training process, lightweight models (0.5B parameters) can also achieve satisfactory math reasoning capabilities, and the entire process is compatible with CPU operation, greatly lowering the participation threshold.

## Two-Stage Training Process and Model Selection

### Two-Stage Alignment Training Process
1. **Supervised Fine-Tuning (SFT)**: Using the MetaMathQA dataset (2000+ math problems with chain-of-thought), teach the model to understand problem structures and generate standardized solutions.
2. **Direct Preference Optimization (DPO)**: No reward model needed; use positive/negative sample pairs (correct reasoning vs. incorrect reasoning) to let the model learn preferences and internalize correct reasoning patterns.

### Model Selection
Trained based on **Qwen2.5-0.5B** for the following reasons:
- High parameter efficiency, trainable on consumer-grade hardware
- Strong base capability, excellent performance in benchmark tests
- Open-source friendly with lenient license agreement

An adapted GPT-2 version is also provided for comparison.

## Dataset Construction and Interactive Web Interface

### Dataset Construction
- **SFT Dataset**: From MetaMathQA, 2000+ instruction-response pairs with detailed chain-of-thought, covering various problem types.
- **DPO Dataset**: Construct positive/negative sample pairs, where positive examples are correct solutions and negative examples are common error patterns.

### Interactive Web Interface
- **Flask API Backend**: RESTful design, supports service deployment with scalable architecture.
- **Streamlit Frontend**: Intuitive interaction, real-time display of reasoning processes, supports parameter adjustment and result comparison.

## Training Results and Evaluation

The project provides detailed evaluation results comparing the performance of the base model, SFT model, and DPO model:
- **Base Model**: Basic language understanding but limited math reasoning ability
- **SFT Model**: Learns answer formats and generates structured responses
- **DPO Model**: Improves answer accuracy and reduces reasoning errors

A quick start script (run_app.bat) is provided for new users to quickly experience the trained models.

## Project Significance and Insights

### Practical Significance
1. **Lower Threshold**: CPU-compatible training process allows more developers to participate in fine-tuning
2. **Methodology Demonstration**: The two-stage alignment process (SFT+DPO) can be replicated in other fields
3. **Data Importance**: High-quality structured data is more effective than increasing parameter count
4. **Open-Source Ecosystem**: Based on Qwen and public datasets, fully reproducible

### Summary
Math Reasoning Arena is an excellent case of end-to-end training, providing a complete solution from data preparation to deployment, and is an ideal starting point for getting into large model fine-tuning.