From RL to LLMs: Optimizing AI with GRPO, PPO, and DPO for Better Fine-Tuning

For decades, Reinforcement Learning (RL) has been the driving force behind breakthroughs in robotics, game-playing AI (AlphaGo, OpenAI Five), and control systems. RL’s strength lies in its ability to optimize decision-making by maximizing long-term rewards, making it ideal for problems requiring sequential reasoning. However, large language models (LLMs) initially relied on supervised learning, where models were fine-tuned on static datasets. This approach […]