Group Relative Policy Optimization (GRPO) is an emerging reinforcement learning algorithm that extends PPO by leveraging group-based advantage estimation. This article offers a thorough code-level walkthrough of GRPO as implemented in the OpenClaw-RL framework. The author breaks down the core components: how GRPO groups trajectories, computes advantages relative to group baselines, and updates policies more stably. Key differences from PPO include the use of a group-level baseline instead of a value function, which reduces variance in policy gradients. For RL researchers and engineers working on agentic AI, understanding GRPO's mechanics is crucial for building more sample-efficient and stable training loops. The post also discusses practical considerations such as batch size tuning and reward normalization in group settings. While the code examples are specific to OpenClaw-RL, the concepts generalize to other RL frameworks.
A detailed source code analysis of GRPO in OpenClaw-RL, comparing it with PPO and explaining group-based advantage estimation.