A new paradigm in large language model (LLM) development is emerging with the introduction of Reinforcement Fine-Tuning (RFT) powered by the Group Relative Policy Optimization (GRPO) algorithm. This approach, highlighted by experts including Andrew Ng and the DeepLearningAI team in collaboration with Predibase, enables LLMs to improve performance on complex reasoning tasks such as math problem solving, coding, and word games. The method enhances LLM capabilities beyond traditional prompt-based interactions by leveraging higher-dimensional feedback mechanisms, as noted by Andrej Karpathy, who described system prompt learning as a critical advancement. A free short course on Reinforcement Fine-Tuning with GRPO has been launched to make this technology accessible, allowing users to transform small open-source LLMs into specialized reasoning tools tailored to specific use cases. Additionally, guidance on optimizing LLM generation settings has been shared to improve output quality, emphasizing that model behavior is influenced by probability distributions rather than just prompt content.