I recently complied and went through a reading / watching list, going from basic NLP to ChatGPT:

- NLP Demystified to learn NLP, especially transformers

- Medium article nicely summarizing the main points of GPT-1, 2 and 3

- GPT-1 lecture and GPT-1 paper to learn about general idea of GPT-like models

- GPT-2 lecture and GPT-2 paper to learn about large scale self-supervised pretraining that fuels GPT training

- GPT-3 lecture 1 and GPT-3 lecture 2 and GPT-3 paper to learn about GPT-3

- InstructGPT page and InstructGPT paper to learn about InstructGPT, the sibling model of ChatGPT; as far as I understand, this is the same as "GPT-3.5"

- ChatGPT page to learn about differences between InstructGPT and ChatGPT, which are relatively small as far as I understand; it is also sometimes called "fine-tuned GPT-3.5", AFAIK

Bonus reading (heavy math warning, experience with RL required!):

- the main difference between GPT-3 and InstructGPT/ChatGPT is reinforcement learning with human feedback (RLHF)

- RLHF is based on Proximal Policy Optimization algorithm

- PPO page and PPO paper