State of GPT: Pretraining, SFT, Reward Modeling & RLHF Pipeline
Andrej KarpathyDistinguishedIndependent / Eureka Labs
AI Researcher & Educator
42:40·2023·undergraduate level
Crisp architectural map of the four-stage LLM training pipeline (base pretraining, supervised finetuning, reward modeling, RLHF) plus practical inference psychology on token-by-token compute.
Learn about the training pipeline of GPT assistants like ChatGPT, from tokenization to pretraining, supervised finetuning, and Reinforcement Learning from Human Feedback (RLHF). Dive deeper into practical techniques and mental models for the effective use of these models, including prompting strategies, finetuning, the rapidly growing ecosystem of tools, and their future extensions.
Source: Microsoft Developer via YouTubeWatch on YouTube
Previous · Lecture 7
Let's Build GPT: From Scratch, in Code, Spelled Out
Next · Lecture 9Let's Build the GPT Tokenizer: Byte Pair Encoding & Unicode Internals
Comments
Add a comment...Sign in
