‘TowardsAI.net’ – aims to democratize artificial intelligence education and foster community of informed and engaged AI practitioners

Towards AI is a prominent platform dedicated to advancing the field of artificial intelligence through a variety of services and offerings. Here’s an overview of what they provide:

  1. Educational Content

Articles and Tutorials: Towards AI publishes a wide range of articles, tutorials, and guides authored by experts in the AI community. These resources cover topics such as machine learning, deep learning, natural language processing, and computer vision, catering to both beginners and seasoned professionals.

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Contributor Platform: The platform encourages AI enthusiasts and professionals to contribute their insights and research. By providing a space for contributors, Towards AI fosters a collaborative environment where knowledge sharing is paramount.

  1. AI News and Updates

Industry News: Towards AI keeps its audience informed about the latest developments in the AI industry, including breakthroughs, research findings, and technological advancements.

  1. Research and Analysis

In-Depth Analyses: The platform offers comprehensive analyses on various AI topics, providing readers with a deeper understanding of complex subjects.

  1. AI Tools and Resources

Curated Resources: Towards AI provides access to a curated list of AI tools, libraries, and datasets, assisting practitioners in their projects and research endeavors.

Towards AI

Group Relative Policy Optimization (GRPO) Illustrated Breakdown & Explanation

 January 31, 2025

Author(s): Ebrahim Pichka

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 usage Uses group sampling for more efficient stable advantage estimation Uses a more conservative update… Read the full blog for free on Medium.