Group Relative Policy Optimization (GRPO) is an online reinforcement-learning method for training language models. For each prompt, it samples several responses, scores them, and uses their rewards relative to one another to guide a policy update. The original method avoids PPO’s separately learned value-function baseline, but it still requires repeated response generation, reward scoring, and careful evaluation.
What is GRPO?
GRPO trains a language model from sampled completions and reward signals. Rather than estimate a value for each state with a separately trained critic, the original method compares multiple completions generated for the same prompt. A response that scores above its group’s average receives a positive relative signal; one that scores below the average receives a negative signal.
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That comparison is local to a prompt’s group. It does not make rewards calibrated across prompts, nor does it guarantee that a reward function measures the quality you actually care about. GRPO is an online method: the current policy generates data that is scored and used to update that policy. The Hugging Face TRL documentation describes this as iterative learning from data generated by the trained model itself.
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How does GRPO work?
- Sample prompts. Draw prompts from the training data that represent the task the model should learn.
- Generate a group of completions. Sample multiple responses for each prompt. Having several responses for the same prompt is essential to the relative comparison.
- Score each completion. Apply one or more reward functions or reward models to the responses.
- Compute relative advantages. Compare rewards within each prompt’s group. Conceptually, a simple centered signal is a completion’s reward minus the group’s mean reward. Implementations can also scale or aggregate rewards in different ways.
- Update the policy. Use a clipped policy-optimization objective to increase the probability of better-scoring outputs relative to worse-scoring group members. The objective may also include KL regularization, depending on the formulation and configuration.
The original GRPO paper presents the group mean as a baseline. Standard-deviation scaling and other normalization choices are implementation decisions, not a universal guarantee of the original mechanism. For the foundational formulation, see DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.
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How does GRPO differ from PPO?
Both methods use policy optimization with sampled actions and can use clipped updates. The central difference in the original proposals is how they obtain the advantage baseline: PPO commonly uses a learned value function, while GRPO uses the relative rewards of multiple completions for one prompt.
| Aspect | PPO | GRPO |
|---|---|---|
| Advantage baseline | Typically estimated by a learned value function. | Derived from rewards of multiple completions for the same prompt. |
| Separate critic | Typically requires a value/critic model, adding model and memory overhead. | Original formulation avoids a separately learned value-function baseline. |
| Rollout work | Requires sampled experience for policy learning. | Requires multiple completions per prompt, plus scoring those completions. |
| Reward dependence | Depends on the chosen reward and training setup. | Also depends on reward quality, and on whether sampled group members provide a useful comparison. |
Removing the separate critic does not remove the main cost of online reasoning training. Generating and scoring several completions can consume substantial inference compute. Nor should “no critic” be read as “no other model”: a configuration can still use a reference model for KL regularization, as well as reward models or other scoring components.
What the original DeepSeekMath results show—and do not show
The DeepSeekMath authors reported 51.7% on the competition-level MATH benchmark without external toolkits or voting, and 60.9% on MATH using self-consistency over 64 samples. They also reported 120 billion math-related pretraining tokens. These are results for the paper’s model and experimental setup, not portable estimates of what another GRPO run will achieve.
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- Use scikit-learn to track an example ML project end to end
- Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
- Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
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- Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
The paper attributes the model’s capability to both math-data selection and GRPO, alongside the model and training setup. The benchmark figures therefore do not isolate GRPO as the sole cause, and they should not be treated as promises for a different model, reward, dataset, or reproduction. The paper and results are described in the original DeepSeekMath publication.
How to train a language model with GRPO
1. Define the task and reward
Write down what constitutes a successful completion before choosing an optimizer configuration. For tasks with objectively checkable answers, an exact-match or other verifiable reward can be appropriate. For open-ended tasks, a learned reward model or multiple reward components may be necessary. In either case, examine scored examples for loopholes: a model can learn to maximize the reward as implemented rather than satisfy the intended task.
2. Prepare prompts and rollout settings
Use representative training prompts and decide the number of completions per prompt, sampling temperature, and maximum completion length. These settings affect both the information in each group and the quantity and diversity of generated text. If responses are too similar, their relative rewards may provide little useful contrast; if outputs routinely hit length limits, truncation can distort training.
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3. Choose the objective and pin the software version
Decide explicitly on the loss variant, clipping behavior, reward scaling, and any KL control. Current rolling TRL documentation lists several loss types and normalization strategies, and identifies DAPO as its current default loss type. These are library choices that can change; they are not timeless definitions of GRPO. Record the package version and configuration with each run, and verify the corresponding documentation for that release.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchTRL’s current documentation describes group standard-deviation scaling as its default and also exposes batch-level and no-scaling options. Standard-deviation scaling can introduce a question-level difficulty bias; with no scaling, update magnitude depends directly on raw rewards and batch composition. Test these choices against the task instead of assuming one is best for every dataset.
4. Budget for generation and scoring
Estimate rollout generation and reward-scoring costs as well as backward-pass memory. GRPO’s critic savings do not account for the extra completions generated per prompt. TRL can use vLLM for rollout generation. The vLLM guide for Transformers Reinforcement Learning documents server mode with dedicated inference GPUs and a colocated mode. Dedicated inference resources can help with throughput and isolation; colocating inference and training may suit different hardware constraints.
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When generation uses an inference engine, check how its sampled-token log probabilities are reconciled with log probabilities recomputed during training. Current TRL documentation exposes importance-sampling correction options for vLLM; the appropriate configuration depends on the software versions and rollout setup.
5. Run a documented starter example
The current TRL quick start loads the training split of trl-lib/DeepMath-103K, creates a GRPOTrainer using Qwen/Qwen2.5-0.5B-Instruct and an accuracy reward, then calls train(). TRL estimates approximately one day distributed across eight GPUs for that documented example. Treat this as the estimate for that example, not as a general hardware requirement or a portable benchmark. Follow the current GRPO Trainer documentation for the release-specific setup and configuration.
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6. Evaluate beyond the training reward
Keep held-out prompts and task-level metrics separate from training. Compare the trained model with its starting checkpoint and simple baselines under the same evaluation protocol. Inspect reward distributions, completion lengths, truncation rates, and policy behavior alongside benchmark scores. A rising reward alone cannot establish that the model is improving at the intended task.
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Implementation choices that can change the result
| Choice | Why it matters | What to check |
|---|---|---|
| Reward scaling | Changes the size and interpretation of group-relative updates; standard-deviation scaling can bias comparisons across question difficulty. | Compare reward distributions and held-out task outcomes under the selected scaling strategy. |
| Loss variant and normalization | Variants such as GRPO, DAPO, Dr. GRPO, and BNPO can differ in token or sequence normalization and clipping behavior. | Record the exact loss type and library version; do not assume variants are interchangeable. |
| KL regularization | Controls how the updated policy is constrained relative to a reference policy when enabled. | In current TRL documentation, beta=0.0 is the default, so no reference model is loaded for a KL term unless KL regularization is enabled. |
| Length and truncation handling | Normalization and truncation treatment can change how long completions affect the update. | Monitor completion lengths and truncation; check whether truncated completions are masked in the chosen configuration. |
| Rollout and training log probabilities | Inference-time sampling and training-time recomputation may not align exactly. | Verify the inference-engine integration and any available importance-sampling correction options. |
| Distributed implementation | Training and inference stacks involve different throughput, resource, and reproducibility trade-offs. | Validate the full configuration on the target hardware and software versions. |
The Allen Institute for AI’s Open Instruct GRPO guide documents an OLMo-core implementation using Ray for distributed training with vLLM inference, as well as a faster DeepSpeed-based variant. These are examples of distinct implementation stacks, not evidence that one is best for every deployment.
When is GRPO a good fit?
GRPO is worth considering when you can sample multiple responses for each prompt and score them with a reward signal that meaningfully distinguishes better from worse outputs. It is especially natural for tasks with verifiable outcomes, but it is not restricted to them. The practical decision is a trade-off: avoid a separately learned value-function baseline in the original formulation, while paying for repeated generation and scoring and taking responsibility for the reward and normalization choices.
Before scaling up, confirm that group members vary enough to produce informative comparisons, that the reward tracks the task rather than a shortcut, and that held-out behavior improves. Compare options using critic memory, rollout cost, reward design, scaling, clipping, KL control, length handling, inference compatibility, reproducibility, and task-specific evaluation—not a headline result from a different experiment.
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