Unleashing the Reasoning Potential of LLMs by Critique Fine-Tuning on One Problem

Abstract

Critique Fine-Tuning (CFT) has recently emerged as a promising paradigm for unlocking the reasoning capabilities of large language models (LLMs). In this work, we introduce one-shot CFT, a highly compute-efficient approach that leverages critique data generated from a single math problem. Remarkably, this method yields significant gains in reasoning accuracy, surpassing one-shot RLVR (Reinforcement Learning with Verifiable Reward) while requiring 15 to 20 times less compute. Given one math problem, we first prompt a set of diverse small models to produce candidate solutions, then use frontier models such as GPT-4.1 to generate high-quality critiques of these responses. We fine-tune Qwen and Llama family models ranging from 1.5B to 14B parameters with CFT. With just 5 GPU hours, our models achieve up to a 16 percent absolute improvement in average accuracy across six mathematical reasoning benchmarks (for example, Qwen2.5-Math-7B improves from 26 percent to 42 percent). Furthermore, ablation studies reveal the robustness of one-shot CFT across different prompt problems. Our findings suggest an extremely compute-efficient approach to unleash the reasoning potential of LLMs.