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
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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

AI Papers Academy

DeepSeek-R1 is an open-source reasoning model trained primarily via large-scale reinforcement learning, offering a detailed alternative to OpenAI's closed-source o1. The paper introduces two models: DeepSeek-R1-Zero, trained without supervised fine-tuning using rule-based Group Relative Policy Optimization (GRPO), and DeepSeek-R1, which adds a four-phase training pipeline including cold-start SFT, reasoning RL, rejection sampling, and a final RL alignment phase. DeepSeek-R1-Zero achieves performance comparable to o1 on reasoning benchmarks like AIME, and notably develops self-correction and extended chain-of-thought reasoning naturally through RL. DeepSeek-R1 addresses readability and language-mixing issues of the Zero variant. Smaller distilled models are also released, making high-capability reasoning accessible at lower parameter counts.

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