Group Relative Policy Optimization (GRPO) Illustrated Breakdown & Explanation

Author(s): Ebrahim Pichka

Originally published on Towards AI.

A simplified intro to GRPO, an efficient policy optimization method used for LLM reasoning training

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Reinforcement Learning (RL) has emerged as a powerful tool for enhancing Large Language Models (LLMs) after their initial training, particularly in reasoning-intensive tasks. DeepSeek’s recent breakthroughs with DeepSeek-Math [2] and DeepSeek-R1 [3] models have demonstrated the remarkable potential of RL in improving mathematical reasoning and problem-solving abilities of LLMs.

These achievements were made possible through an innovative RL approach called Group Relative Policy Optimization (GRPO), which addresses the unique challenges of applying RL to language models. In this post, we’ll dive deep into how GRPO works and why it represents a significant advancement in LLM training.

Proximal Policy Optimization (PPO) [1] has been the go-to algorithm for RL fine-tuning of language models. At its core, PPO is a policy gradient method that uses clipping to limit policy updates (gradients), preventing destructive large policy changes. The objective function for PPO can be written as:

GRPO — first introduced in [2] — builds upon PPO’s foundation but introduces several key innovations that make it more efficient and better suited for language models:

Eliminates the need for a value network, hence less memory/compute usageUses group sampling for more efficient stable advantage estimationUses a more conservative update… Read the full blog for free on Medium.

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Published via Towards AI