Group Relative Policy Optimization (GRPO) is an RL fine-tuning method that compares multiple completions for the same prompt against each other rather than against an absolute reward model, reducing variance and cost. By ranking outputs within a group, GRPO eliminates the need for a separate trained reward model and produces more stable training signals.