GRPO: The Algorithm Behind DeepSeek's Success [A Practical Introduction]

Group Relative Policy Optimization (GRPO) is a reinforcement learning technique developed by DeepSeek to address the limitations of traditional methods like PPO (Proximal Policy Optimization). PPO relies on a dual-model setup - policy and critic—which introduces memory inefficiency, training instability, and implementation complexity, especially when scaling to large language models (LLMs). GRPO simplifies this by removing the critic and using relative comparisons within groups of outputs to guide learning.

Key Ideas

GRPO Eliminates the Critic Model for Efficiency and Stability: Instead of estimating absolute values, GRPO compares multiple outputs for the same input and uses their average reward as a baseline. This reduces memory usage and improves training stability, especially in high-dimensional output spaces like LLMs.
GRPO Enables Reliable Learning Through Relative Comparison: By focusing on group-based reward signals, GRPO avoids the pitfalls of unstable value estimation. This comparative approach is more robust to outliers and easier to calibrate, leading to smoother training dynamics and fewer divergence issues.

Why It Matters?

GRPO is easier to implement and more resource-efficient than PPO, making it ideal for training LLMs without enterprise-grade infrastructure. GRPO’s simplicity and scalability make it attractive for startups aiming to fine-tune models on limited hardware. GRPO success also represents a shift toward leaner, more scalable RL methods.