GRPO

GRPO samples multiple completions for a prompt and uses their rewards to estimate relative advantages, commonly by centering and scaling scores within the group. It avoids a separately trained value model in the original formulation, using the group as its baseline instead. A clipped policy objective limits updates relative to the sampling policy, and formulations may include a penalty for divergence from a reference. Group size, generation settings and reward variability affect the learning signal. Modern implementations offer normalization and loss variants, so the exact objective needs documentation. GRPO does not define whether correctness, human preference or another property supplies the reward.

Test reward functions independently and select prompts where sampled completions exhibit meaningful quality differences. Choose group size and generation limits within the available inference and training budget. Inspect zero-variance groups, reward distributions, completion lengths, clipping and divergence. Keep problem families and solution sources separate from evaluation. Record the implementation's loss and normalization settings, then assess independently verified task accuracy rather than average reward alone. The useful result is a policy improvement with a reproducible sampling and optimization recipe and evidence that the relative signal corresponds to the intended behavior.

Relative normalization can interact with prompt difficulty and reward variance, while group sampling adds substantial generation cost. Identical rewards within a group provide weak or absent differentiation. A flawed verifier or biased reward model still produces flawed optimization. Length effects and normalization choices can influence which responses receive pressure. GRPO is neither a guarantee of reasoning nor a synonym for reinforcement learning from verifiable rewards. Evaluate the reward source, policy objective and task behavior as separate components.