TechnologyFine-Tuning & Adaptation
GRPO
At a glance
PPO without the value model: sample a group of answers, score them against each other, push toward the better ones.
- Who this is for
- Engineers and technical readers learning the terms used in AI systems.
- Topics
- Fine-Tuning & Adaptation
- Technology
In short
GRPO, Group Relative Policy Optimization, is a reinforcement learning method that samples several answers to one prompt, scores them, and moves the model toward the answers that beat the group's average, with no separate value model. Bigcircle recommends building the verifier first and using it at inference to pick a passing answer, because that shows whether the check is strict enough before any training run learns to game it.
Key facts
Definition
GRPO is a variant of PPO, introduced in DeepSeekMath in February 2024, that uses the other answers sampled for the same prompt as the baseline instead of a learned value model about the size of the policy.
How Bigcircle does it
- Bigcircle recommends using a verifier at inference before training with it, sampling a few answers and keeping the one that passes, because the failure log shows how a model could cheat the check.
- Track the share of groups where every answer scores the same, mean response length beside accuracy, and a hand-read sample of high-reward answers, because those catch the three documented failure modes.
Comparison
PPO trains a separate value model to estimate expected reward, which costs memory on a large policy, while GRPO drops that network and compares answers drawn for the same prompt.
Key number
As of October 2026, Hugging Face's TRL GRPOTrainer defaults to the DAPO loss, 8 answers per prompt and a KL penalty of zero.
GRPO, Group Relative Policy Optimization, is a reinforcement learning method for language models. For each prompt it samples a group of answers, scores every answer with a reward, and moves the model toward the answers that scored above the group's average and away from those below it. DeepSeek introduced it in the DeepSeekMath paper in February 2024 as a variant of PPO, and it became widely known a year later as the method behind DeepSeek-R1.
GRPO saves memory over PPO by changing the baseline. PPO trains a second network, a value model, to estimate how good an answer should be, and that network is about as large as the policy itself. GRPO drops it and uses the other answers in the group as the baseline instead. One model fewer in memory is a real saving when the policy has billions of parameters.
Why it took off with reasoning models#
DeepSeek-R1 showed that reasoning can be trained with reinforcement learning on rewards a program can check: a math answer that matches, code that passes its tests, output in the required format. With GRPO and those rule-based rewards, the model learned behaviours such as checking its own work and trying another approach, without being shown examples of either. The paper was later published in Nature.
Check the reward before you believe the result. GRPO only compares answers against each other, so it learns whatever the reward prefers. Where a check is exact, that is the right answer. Where the check is loose, the model learns to pass the check.
Where it goes wrong#
Three failure modes are well documented, and the first two have published fixes.
Groups with no spread teach nothing. If every answer in a group gets the same reward, all right or all wrong, every advantage is zero and the group contributes zero gradient. On easy prompts the model wastes compute, and on prompts it cannot yet solve it gets no signal at all. DAPO's dynamic sampling filters those groups out and keeps sampling until each batch has groups with mixed results. Track the share of zero-spread groups during a run, because a rising share means the prompts have become too easy or too hard for the model.
Length creeps up. Liu et al. found that the way original GRPO normalizes by response length biases training toward longer answers, most of all on wrong answers. Their Dr. GRPO removes that normalization. DAPO uses a token-level loss for a related reason, and adds a penalty for answers that run past the length limit. Watch mean response length beside accuracy, and treat length rising while accuracy stays flat as this bias, not as the model learning to think harder.
Reward hacking. A verifier with a gap gets found. A unit test suite that does not check edge cases rewards code that passes the visible tests and nothing else. Read a sample of high-reward answers by hand at every checkpoint, because a reward that is being gamed still reports success.
The trainer you pick has already chosen among these. As of October 2026, Hugging Face's TRL GRPOTrainer defaults to the DAPO loss, samples 8 answers per prompt, and sets the KL penalty to zero unless you turn it on. Read the defaults before you report that you trained "with GRPO", because two runs under that name can differ in all three.
The verifier is the part to build first#
Most product teams will not need to train with GRPO. What they do need is the verifier. On our DevOps agent, every step the agent takes is either checkable by a machine or reviewed by a person, and a validation loop of structural and semantic checks catches most bad templates before AWS is asked to apply them. A check like that is exactly what GRPO would need as a reward.
Bigcircle recommends building that check and using it at inference before considering training: sample a few answers, keep the one that passes, and log the failures. That uses the verifier with no training run to maintain, and it tells you whether the check is strict enough. If a model trained against the check would learn to cheat it, the log shows you how, before a training run does. Move to GRPO, usually in the RLVR setting where a program supplies the reward, only when sampling at inference costs too much at your volume and the reward has held up against real outputs.