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ai-modelscope
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README.md
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README.md
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license: Apache License 2.0
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#model-type:
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##如 gpt、phi、llama、chatglm、baichuan 等
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#- gpt
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#domain:
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##如 nlp、cv、audio、multi-modal
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#- nlp
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#language:
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##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
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#- cn
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#metrics:
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##如 CIDEr、Blue、ROUGE 等
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#- CIDEr
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#tags:
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##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
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#- pretrained
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#tools:
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##如 vllm、fastchat、llamacpp、AdaSeq 等
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#- vllm
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language:
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- en
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base_model:
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- meta-llama/Llama-3.2-3B-Instruct
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tags:
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- One-Shot-CFT
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---
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### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重，可浏览“模型文件”页面获取。
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#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型
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# One-Shot-CFT: Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem
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SDK下载
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```bash
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#安装ModelScope
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pip install modelscope
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```
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```python
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#SDK模型下载
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from modelscope import snapshot_download
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model_dir = snapshot_download('TIGER-Lab/One-Shot-CFT-Math-Llama-3B')
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```
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Git下载
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```
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#Git模型下载
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git clone https://www.modelscope.cn/TIGER-Lab/One-Shot-CFT-Math-Llama-3B.git
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```
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<p align="center">
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<a href="https://github.com/TIGER-AI-Lab/One-Shot-CFT" target="_blank">💻 Code</a> |
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<a href="https://arxiv.org/pdf/2506.03295" target="_blank">📄 Paper</a> |
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<a href="https://huggingface.co/datasets/TIGER-Lab/One-Shot-CFT-Data" target="_blank">📊 Dataset</a> |
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<a href="https://huggingface.co/collections/TIGER-Lab/one-shot-cft-683fbb4d2bcf698dbea8fb21" target="_blank">🤗 Model</a> |
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<a href="https://tiger-ai-lab.github.io/One-Shot-CFT/" target="_blank">🌐 Project Page</a>
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</p>
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<p style="color: lightgrey;">如果您是本模型的贡献者，我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>，及时完善模型卡片内容。</p>
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## 🧠 Overview
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One-Shot Critique Fine-Tuning (CFT) is a simple, robust, and compute-efficient training paradigm for unleashing the reasoning capabilities of pretrained LLMs in both mathematical and logical domains. By leveraging critiques on just one problem, One-Shot CFT enables models like Qwen and LLaMA to match or even outperform reinforcement learning, while using 20× less compute.
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Instead of learning from reference answers (as in supervised fine-tuning) or reward signals (as in reinforcement learning), One-Shot CFT enables models to learn from critiques of diverse solutions to a single problem, enhancing their exposure to varied reasoning patterns and mitigating overfitting. This exposes the LLMs to multiple perspectives and error types, thereby more effectively unleashing their reasoning potential.
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## ✨ Key Highlights
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- **Unleashes Reasoning with One Example:** One-Shot CFT uses critiques of diverse model-generated solutions to a single problem to significantly boost performance across math and logic tasks. For example, with just 5 GPU hours of training on Qwen2.5-Math-7B, One-Shot CFT achieves an average improvement of +15% on six math benchmarks and +16% on three logic reasoning benchmarks.
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- **Outperforms RLVR and Full SFT with 20× Less Compute:** One-Shot CFT outperforms both one-shot Reinforcement Learning with Verifiable Rewards (RLVR) and full-dataset supervised fine-tuning, while requiring only 5 GPU hours on a 7B model—offering a much more efficient and stable training alternative.
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- **Robust Across Seeds and Model Scales:** One-Shot CFT remains effective across different seed problem choices and model sizes—from 1.5B to 14B parameters—demonstrating strong generalization and scalability.
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**This specific model is the One-Shot CFT variant trained based on [Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) with [DSR-CFT-p0](https://huggingface.co/datasets/TIGER-Lab/One-Shot-CFT-Data) dataset.**
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## Main Results
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<p align="center">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/636a35eff8d9af4aea181608/DCxRSdeDrv-Db4VLuEl0T.png" alt="CFT Performance Comparison" width="1100"/>
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</p>
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<p align="center"><em>
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One-shot CFT consistently improves mathematical and logical reasoning.
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<strong>Left:</strong> Average accuracy on six mathematical reasoning benchmarks for Qwen and LLaMA models, comparing base, SFT, RLVR, and CFT with only one training example.
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<strong>Right:</strong> In-domain accuracy on three logic reasoning benchmarks (BBEH subtasks) for Qwen2.5-Math-7B.
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Across both domains, CFT with a single problem significantly outperforms standard SFT and matches or exceeds reinforcement learning with much lower compute.
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</em></p>
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## Citation
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If you find our work helpful, please cite it as:
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```bibtex
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@article{wang2025unleashing,
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title={Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem},
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author={Wang, Yubo and Nie, Ping and Zou, Kai and Wu, Lijun and Chen, Wenhu},
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journal={arXiv preprint arXiv:2506.03295},
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year={2025}
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}
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```
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