A Practical Guide to RL Behind RLHF and GRPO

Suvash Sedhain wrote an intuition-first walkthrough of the reinforcement learning stack that powers modern LLM alignment, covering the path from basic REINFORCE through advantage functions, GAE, PPO, and GRPO. The structure is deliberate – each concept gets introduced only when the previous one breaks down, which makes the credit assignment problem at the core of token-level optimization much easier to follow than most academic treatments. The PPO versus GRPO comparison is particularly useful: PPO needs three to four model-equivalents running simultaneously while GRPO drops the critic entirely in favor of group-level statistics. Understanding this pipeline has become table stakes for anyone working on post-training, and clear explanations at this level remain surprisingly rare given how central RLHF has become to every major lab’s workflow.