DeepSeek-R1 Dissection: Understanding PPO & GRPO Without Any Prior Reinforcement Learning Knowledge

The article provides an in-depth exploration of reinforcement learning (RL) concepts, using intuitive analogies to explain complex methodologies such as Proximal Policy Optimization (PPO) and Group Relative Policy Optimization (GRPO) without requiring prior RL knowledge. It elaborates on how relying solely on absolute rewards can lead to unfairness and instability, emphasizing the role of the Critic in providing relative performance baselines and how mechanisms like the Clip operation prevent over-optimization. Additionally, the piece highlights the importance of maintaining a balance between exploration and stability through reference models that discourage extreme strategies. GRPO is introduced as an evolution of PPO, leveraging multiple simulated averages to eliminate the need for a separate value function, thus reducing computational resources while maintaining effective training dynamics. The article effectively uses the metaphor of an elementary school exam to simplify these advanced concepts, making them accessible to a broader audience.