Build a Reasoning LLM using GRPO

Group Relative Policy Optimization (GRPO) enhances language models' reasoning abilities without labeled data. This reinforcement learning method generates multiple responses, assigns rewards using deterministic functions, and updates the model through backpropagation. It adds reasoning-focused prompts, evaluates responses with format and accuracy-checking reward functions, and applies GRPO loss functions. The approach is demonstrated using UnslothAI and HuggingFace TRL with the Qwen3-4B-Base model on a math dataset.