The pre-trained model is stage 1 - it has seen everything, but it is wild. If you ask it "What is the capital of US?" it will reply "What is the capital of Canada?"...
Stage 2 is task solving practice. We use 1000-2000 supervised datasets, formatted as prompt-input-output texts. They could be anything: translation, sentiment classification, question answering, etc. We also include prompt-code pairs. This teaches the model to solve tasks (it "hires" this ability from the model). Apparently training on code is essential, without it the model doesn't develop reasoning abilities.
But still the model is not well behaved, it doesn't answer in a way we like. So in stage 3 it goes to human preference tuning (RLHF). This is based on human preferences between pairs of LLM answers. After RLHF it learns to behave and to abstain from certain topics.
You need stage 1 for general knowledge, stage 2 for learning to execute prompts, stage 3 to make it behave.
Regarding Stage 2. Are you saying that ChatGPT's facility to recognize and process commands is derived entirely from training on supervised datasets and not hand-crafted logic? Can you point me to any reading on this?
Stage 2 is task solving practice. We use 1000-2000 supervised datasets, formatted as prompt-input-output texts. They could be anything: translation, sentiment classification, question answering, etc. We also include prompt-code pairs. This teaches the model to solve tasks (it "hires" this ability from the model). Apparently training on code is essential, without it the model doesn't develop reasoning abilities.
But still the model is not well behaved, it doesn't answer in a way we like. So in stage 3 it goes to human preference tuning (RLHF). This is based on human preferences between pairs of LLM answers. After RLHF it learns to behave and to abstain from certain topics.
You need stage 1 for general knowledge, stage 2 for learning to execute prompts, stage 3 to make it behave.