Reinforcement Fine-Tuning (RFT) emerges as a promising alternative to Supervised Fine-Tuning (SFT) by leveraging reinforcement learning for tasks that require model performance improvement in specific domains, even when labeled data is scarce. Unlike SFT, which relies on static datasets and can suffer from overfitting, RFT uses an online approach where models learn through reward-based feedback, allowing them to refine strategies in real-time without explicit labels. This makes RFT particularly effective for tasks benefiting from Chain-of-Thought (CoT) reasoning, as it encourages models to develop new reasoning strategies rather than memorizing fixed answers. Various algorithms, such as Group Relative Preference Optimization (GRPO), enhance RFT's efficiency by optimizing model outputs based on relative preference rankings. Experiments demonstrate RFT's superiority in scenarios with limited data, and its ability to improve reasoning tasks, like the Countdown game, by enabling models to adapt and refine their decision-making processes dynamically.