Group Relative Policy Optimization (GRPO) is a reinforcement learning algorithm designed to optimize policy training in large language models and other complex AI systems. As a successor to Proximal Policy Optimization (PPO), GRPO addresses critical computational and memory constraints that emerge when training state-of-the-art reasoning models at scale. The algorithm achieves this by replacing traditional value function estimation with advantage estimates derived from group comparisons across multiple rollouts.
GRPO extends the PPO framework by fundamentally reconceiving how advantage estimates are computed during policy optimization. While PPO relies on a separate critic model (value function) to estimate the baseline for advantage calculation, GRPO eliminates this architectural component entirely 1). Instead, GRPO computes advantages by comparing the rewards obtained from multiple rollouts of the same prompt or context, creating a relative ranking within groups rather than absolute value predictions.
The core mathematical insight underlying GRPO involves grouping multiple response samples generated from identical input contexts. Within each group, the algorithm estimates advantages based on the relative ordering of cumulative rewards across samples. This group-relative comparison approach eliminates the need to maintain and train a separate value model, which traditionally constitutes a significant fraction of the total parameters in large-scale RL training pipelines 2).
The advantage estimate for a particular rollout can be expressed relative to the mean or median reward within its group. This enables the policy gradient computation to proceed without fitting an explicit value function, substantially reducing both memory footprint and computational overhead.
The elimination of the critic model represents a transformative efficiency improvement for large-scale RL training. In traditional PPO implementations applied to models with billions or trillions of parameters, maintaining a value function that mirrors the policy model's architecture creates a substantial memory burden. GRPO's group-relative approach avoids this duplication, allowing organizations to train reasoning models with fewer computational resources 3).
This efficiency gain becomes particularly pronounced when training on sequences that require extended computation—such as chain-of-thought reasoning traces, mathematical problem solving, or code generation tasks. By reducing memory requirements, GRPO enables larger batch sizes, longer training runs, or deployment on hardware configurations that would be prohibitive under traditional PPO with separate critic models. The computational savings extend to training time, as the algorithm eliminates the backward passes required to optimize the value function network.
GRPO has emerged as a standard technique for training modern reasoning models that require extensive RL optimization. These models, designed to solve complex problems requiring multi-step reasoning, benefit significantly from the efficiency gains that GRPO provides. The algorithm's ability to work effectively with group-based advantage estimates makes it particularly suitable for scenarios where diverse reasoning paths or solution approaches need to be compared and ranked 4).
Applications include mathematical problem solving, logical reasoning, code generation with correctness verification, and complex question-answering tasks where outputs can be objectively evaluated. The group-relative formulation aligns naturally with these domains, where multiple solution attempts can be generated and ranked without requiring explicit value predictions. GRPO has become the standard approach for large-scale reinforcement learning in reasoning models, significantly reducing the memory and compute overhead that was previously required by traditional value function-based methods. During the PPO era, reinforcement learning from human feedback (RLHF) training relied on separate value models and Generalized Advantage Estimation (GAE), but GRPO has superseded this approach by using group-derived baselines from multiple completions as a more efficient alternative 5).
GRPO offers several key advantages over traditional PPO-based approaches. The primary benefit remains reduced computational overhead through elimination of the critic model, enabling more efficient resource allocation during large-scale training. Additionally, the group-relative advantage estimation may provide more stable gradient signals in certain domains, as comparisons within groups can be more robust to absolute reward scale variations than value function predictions 6).
The algorithm requires careful consideration of group size selection—larger groups provide more reliable advantage estimates but increase the number of forward passes required. Practitioners must balance the trade-off between estimation stability and computational cost when configuring group sizes for specific training scenarios. The quality of reward signals remains critical, as GRPO depends entirely on accurate comparative judgments within groups rather than learning a generalizable value function.