I reverse-engineered OpenAI’s O1-Preview model using O1-Preview! I asked it to generate the full research paper with code and I gave it dozens of related research papers from the past few years as context. It recreated a working version of the O1 model to the best of it’s ability and In this video, we’ll go over all the details of the model, the code, and the research techniques that make the O1 model series state of art across so many benchmarks. LETS SPREAD THIS AI POWER, I can’t wait to see what you think, enjoy!
Code & paper for this video:
https://github.com/llsourcell/o1-nano/
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⏱️ **Chapters:**
0:00 – Introduction: Reproducing OpenAI’s o1 Model Series
1:30 – Generating a Research Paper Using o1 Preview
2:30 – Overview of ‘o1-nano’: An Open Source, Explainable Model
3:30 – Understanding Chain-of-Thought Reasoning in o1 Models
4:30 – How Reinforcement Learning is Used in Training and Inference
5:30 – Exploring Reasoning Paths and Subtasks During Inference
6:30 – Unpacking OpenAI’s Reasoning Tokens
7:30 – Overview of the Model Architecture
8:30 – Core Components: Transformer, Chain-of-Thought Module, Reasoning Token Generator
9:30 – Training the Model to Reason Better Using Reinforcement Learning
13:30 – Historical Papers Leading to o1: Chain-of-Thought and ‘Let’s Verify Step by Step’
15:30 – The New Scaling Law: Inference Time Scaling
16:30 – The Usage of of Reinforcement Learning
17:30 – Demo of the Code: Running the Test
18:30 – Conclusion: Open Source Code and Research Paper as a Starting Point
19:00 – Closing Remarks and Encouragement to Explore the GitHub Repository
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