GRPO Explained: A Lightweight RL Method for Fine-Tuning Large Language Models

Group Relative Policy Optimization (GRPO) is a reinforcement learning technique designed to fine-tune and align large language models more efficiently than traditional methods. Unlike PPO, which requires both a reward model and a critic model, GRPO eliminates the need for a separate critic by generating a group of responses and comparing them using a rule-based reward function. The method optimizes the model's policy by increasing the likelihood of better responses and reducing that of weaker ones, making it computationally lighter. GRPO differs from Supervised Fine-Tuning (SFT), which relies on curated prompt-response datasets and cross-entropy loss to train models. SFT is prone to issues like overfitting and catastrophic forgetting when training data is small, low-quality, or lacks diversity, making GRPO a compelling alternative in such scenarios.