DeepSeek-R1 Paper Explained - A New RL LLMs Era in AI?
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/DeepSe...
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The video was edited using VideoScribe - https://tidd.ly/44TZEiX
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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