DeepSeek-R1 Paper Explained - A New RL LLMs Era in AI?

Video Description

In this video, we dive into the groundbreaking DeepSeek-R1 research paper, titled "DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning". This paper introduces the models DeepSeek-R1-Zero and DeepSeek-R1, open-source reasoning models that rivals the performance of top-tier models like OpenAI's o1! Here's a quick overview of what we'll cover: - Training a Large Language Model (LLM) using Reinforcement Learning (RL) only in post-training, without Supervised Fine-tuning (SFT). - Rule-based Reinforcement Learning (RL) used DeepSeek-R1 for large-scale RL training. - Intriguing insights including the "aha" moment. - DeepSeek-R1 Training Pipeline - Performance results Written review - https://aipapersacademy.com/deepseek-r1/ Paper - https://arxiv.org/abs/2501.12948 Project page - https://github.com/deepseek-ai/DeepSeek-R1/tree/main ----------------------------------------------------------------------------------------------- ✉️ Join the newsletter - https://aipapersacademy.com/newsletter/ 👍 Please like & subscribe if you enjoy this content Become a patron - https://www.patreon.com/aipapersacademy The video was edited using VideoScribe - https://tidd.ly/44TZEiX ----------------------------------------------------------------------------------------------- Chapters: 0:00 Introduction 0:52 LLMs Training 2:20 RL-only LLM (DeepSeek-R1-Zero) 2:53 Rule-based RL 4:41 DeepSeek-R1-Zero Insights 5:41 DeepSeek-R1 Aha Moment 6:09 Training DeepSeek-R1 8:48 DeepSeek-R1 Results