58 lines
2.9 KiB
Markdown
58 lines
2.9 KiB
Markdown
# OmniThought: A Large-Scale Chain-of-Thought Dataset for Advancing Large Reasoning Models
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## Overview
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The rise of **Large Reasoning Models (LRMs)** has revolutionized **Natural Language Processing (NLP)**, enabling breakthroughs in complex tasks like **mathematical problem-solving** and **code generation**. These models rely on **Chain-of-Thought (CoT)** processes to mimic human-like reasoning. However, progress in LRMs is limited by the scarcity of **high-quality, large-scale CoT datasets**—existing resources often lack:
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- **Diverse reasoning problems** with well-structured CoT processes.
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- **Multi-teacher distillation** to ensure reasoning quality.
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- **Fine-grained annotations** describing CoT properties.
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To bridge this gap, we introduce **`OmniThought`**, a **2-million-scale CoT dataset** generated and validated by **two powerful LRMs**. Each CoT process is annotated with:
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- **Reasoning Verbosity (RV)**: Measures the optimal verbosity of reasoning steps.
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- **Cognitive Difficulty (CD)**: Assesses the complexity of reasoning for model comprehension.
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We also propose a **self-reliant pipeline** for dataset curation, ensuring high-quality reasoning traces.
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## Key Features
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✅ **2 million high-quality CoT processes** covering diverse reasoning tasks.
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✅ **RV-CD scores** to guide model training for better reasoning performance.
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✅ **Multi-teacher distillation** for robust and coherent reasoning paths.
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✅ **Optimized for LRM training**—improves reasoning ability and output quality.
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## Experiments & Results
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Extensive experiments with **Qwen2.5 models** (various sizes) confirm that:
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- Training with **RV-CD scores** enhances **LRM reasoning effectiveness**.
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- Models trained on `OmniThought` achieve **stronger reasoning abilities** with **optimal CoT length and difficulty**.
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Based on this dataset, we release **a series of high-performance LRMs** with superior reasoning capabilities.
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## Use the Datasets
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```python
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from datasets import load_dataset
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# Login using e.g. `huggingface-cli login` to access this dataset
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ds = load_dataset("alibaba-pai/OmniThought")
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```
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## Reference
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For more detailed information about the dataset construction process, we encourage you to refer to our paper:
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- **Reasoning with OmniThought: A Large CoT Dataset with Verbosity and Cognitive Difficulty Annotations**
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Wenrui Cai, Chengyu Wang, Junbing Yan, Jun Huang, Xiangzhong Fang
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[arXiv:2505.10937](https://arxiv.org/abs/2505.10937)
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You can cite the paper using the following citation format:
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```bibtex
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@misc{cai2025reasoningomnithoughtlargecot,
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title={Reasoning with OmniThought: A Large CoT Dataset with Verbosity and Cognitive Difficulty Annotations},
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author={Wenrui Cai and Chengyu Wang and Junbing Yan and Jun Huang and Xiangzhong Fang},
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year={2025},
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eprint={2505.10937},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2505.10937}
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}
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``` |