Decoding LLM Alignment: A Concise Guide for GRPO and its variants

Large language model alignment has increasingly shifted from actor-critic reinforcement learning approaches toward group-normalized and sequence-level optimization methods that reduce computational overhead and better support reasoning tasks. The guide compares PPO, which uses a critic for token-level value estimates and remains a standard for RLHF; RLOO, which removes the critic by using other sampled completions as a reward baseline; and GRPO, which evaluates multiple completions relative to their group’s average reward and has been used for math and coding reasoning models. It also covers Pref-GRPO, which replaces raw group scores with pairwise win rates to incorporate subjective preference data; GSPO, which performs clipping and optimization at the full-sequence level to stabilize large-scale mixture-of-experts training; and GDPO, which independently normalizes multiple reward objectives before combining them to preserve tradeoffs among goals such as accuracy, safety, and brevity. The central distinction among these methods is how they construct advantages and process groups of generated completions, making algorithm choice dependent on available compute, reward types, task requirements, and whether alignment involves single or competing objectives.

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