---
title: OmniThought-0528
canonical_url: "https://www.modelscope.cn/datasets/PAI/OmniThought-0528"
md_url: "https://www.modelscope.cn/datasets/PAI/OmniThought-0528.md"
repository: PAI/OmniThought-0528
last_updated: 2025-12-11
license: apache-2.0
storage_size: "13 GB"
downloads: 205
stars: 2
---

# OmniThought-0528

> OmniThought-0528 - PAI 在 ModelScope 开源的数据集。OmniThought-0528 A High-Quality Chain-of-Thought (CoT) Dataset for Enhanced Model Distillation

PAI/OmniThought-0528 是 ModelScope 魔搭社区上的数据集，存储大小 13 GB，采用 apache-2.0 许可。

- **Repository**: PAI/OmniThought-0528
- **License**: apache-2.0
- **Storage size**: 13 GB
- **Downloads**: 205
- **Stars**: 2
- **Last updated**: 2025-12-11

Source: https://www.modelscope.cn/datasets/PAI/OmniThought-0528

---

# OmniThought-0528  
*A High-Quality Chain-of-Thought (CoT) Dataset for Enhanced Model Distillation*  

## **Overview**  
OmniThought-0528 is an advanced version of the **OmniThought** dataset, designed to enhance reasoning capabilities in language models through high-quality **Chain-of-Thought (CoT)** distillation. It consists of **365,000 reasoning chains** across diverse domains, including **mathematics, coding, and science**, generated and rigorously validated using state-of-the-art teacher models.  

## **Dataset Construction**  
### **Data Sources & Processing**  
- **Input Problems**: Collected from multiple public sources, covering **mathematical, scientific, and programming reasoning tasks**.  
- **Teacher Models**: Generated using **DeepSeek-R1** and **QwQ-32B**, then filtered via **"LLM-as-a-Judge"** validation to ensure correctness.  
- **Quality Control**: Only verified reasoning chains were retained, ensuring high reliability.  

### **Annotations & Metrics**  
Each reasoning chain is annotated with two key metrics:  

| **Score** | **Reasoning Verbosity (RV)** | **Cognitive Difficulty (CD)** |  
|-----------|-----------------------------|-------------------------------|  
| **0-1**   | Minimal explanation, direct answers. | Elementary knowledge, single-step reasoning. |  
| **2-3**   | Concise reasoning with necessary steps. | Multi-step arithmetic, rule-based logic. |  
| **4-5**   | Detailed explanations, thorough reasoning. | Basic algebra, non-trivial inference. |  
| **6-7**   | Comprehensive exploration, advanced techniques. | Higher-level methods (e.g., dynamic programming, proofs). |  
| **8-9**   | Deep, exhaustive reasoning with nested arguments. | Highly abstract (e.g., complex algorithm analysis). |  

These metrics help **match reasoning complexity with model size**—smaller models benefit from simpler chains, while larger models leverage advanced reasoning.  

## **Key Features**  
- **Enhanced Teacher Model**: Generated using **DeepSeek-R1-0528**, a top-tier model (comparable to **GPT-4o & Gemini-2.5-Pro**) excelling in **math, coding, and logic benchmarks**.  
- **Structured Format**: Each entry includes:  
  ```json
  {
    "question": "problem_text",
    "reasoning": [
      {
        "full_response": "detailed_answer",
        "Cognitive_Difficulty": score,
        "Reasoning_Verbosity": score
      },
      ...
    ]
  }
  ```