Comprehensive comparison of two prominent reinforcement learning algorithms: Proximal Policy Optimization (PPO) and Generalized Randomized Policy Optimization (GRPO).
It details their algorithmic foundations, highlighting PPO's evolution from traditional policy gradient methods with a focus on stability and computational efficiency through its clipped surrogate objective, and GRPO's emergence as a specialized, critic-free variant designed for fine-tuning large language models (LLMs) by relying on group-based advantage estimation.
The document meticulously analyzes their performance characteristics, emphasizing PPO's strength in continuous control and classic gaming environments versus GRPO's significant memory and computational advantages for LLMs, particularly in reasoning and code generation tasks.
It also addresses implementation challenges, hyperparameter tuning, and explores recent advancements and future research directions for both algorithms, ultimately providing a decision framework for their optimal application based on problem domain and resource constraints.
