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## 🧠 Overview 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. 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. ## ✨ Key Highlights - **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. - **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. - **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. **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-TimeArithmetic-p0](https://huggingface.co/datasets/TIGER-Lab/One-Shot-CFT-Data) dataset.** ## Main Results
One-shot CFT consistently improves mathematical and logical reasoning. Left: Average accuracy on six mathematical reasoning benchmarks for Qwen and LLaMA models, comparing base, SFT, RLVR, and CFT with only one training example. Right: In-domain accuracy on three logic reasoning benchmarks (BBEH subtasks) for Qwen2.5-Math-7B. Across both domains, CFT with a single problem significantly outperforms standard SFT and matches or exceeds reinforcement learning with much lower compute.
## Citation If you find our work helpful, please cite it as: ```bibtex @article{wang2025unleashing, title={Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem}, author={Wang, Yubo and Nie, Ping and Zou, Kai and Wu, Lijun and Chen, Wenhu}, journal={arXiv preprint arXiv:2506.03295}, year={2025} } ```