初始化项目，由ModelHub XC社区提供模型
Model: TIGER-Lab/One-Shot-CFT-Logic-Qwen-7B-DisambiguationQA Source: Original Platform
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---
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base_model:
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- Qwen/Qwen2.5-Math-7B
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language:
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- en
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tags:
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- One-Shot-CFT
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pipeline_tag: text-generation
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library_name: transformers
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license: cc-by-4.0
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---
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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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<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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## 🧠 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 [Qwen2.5-7B-Math](https://huggingface.co/Qwen/Qwen2.5-Math-7B) with [BBEH-CFT-DisambiguationQA-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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207
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Normal file
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Normal file
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File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user