Mastering LLM Fine-Tuning: GRPO, PPO, and DPO Compared
Last Updated on August 29, 2025 by Editorial Team
Author(s): Adi Insights and Innovations
Originally published on Towards AI.
Learning Outcomes
Reinforcement Learning (RL) has led to major advancements in fields such as robotics, game-playing AI, and control systems by focusing on maximizing long-term rewards through sequential decision-making. In their early stages, Large Language Models (LLMs) were primarily trained using supervised learning, which limited their ability to adapt and align with complex human preferences. The emergence of Reinforcement Learning from Human Feedback (RLHF) transformed this landscape, allowing systems like ChatGPT and DeepSeek to refine their outputs based on user interactions. Nevertheless, traditional RLHF methods, particularly those using Proximal Policy Optimization (PPO), encountered drawbacks due to the high costs associated with building and maintaining reward models. DeepSeek’s Group Relative Policy Optimization (GRPO) addresses this challenge by optimizing models directly from preference comparisons, representing a substantial step forward in LLM training efficiency and the evolution of reinforcement learning strategies.
This article explores various reinforcement learning methods for fine-tuning Large Language Models (LLMs). It discusses traditional techniques such as Proximal Policy Optimization (PPO) alongside innovations like Direct Preference Optimization (DPO) and Group Relative Policy Optimization (GRPO) by DeepSeek. The need for efficient, human-aligned output is emphasized, highlighting trends in adapting LLMs to better serve human expectations. Ultimately, it builds a case for GRPO as a promising solution for scalable and effective LLM training without the reliance on traditional reward models.
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