A model trained only to continue text does not answer questions. Four training stages turn it into an assistant.
Here is something that surprises most people. The first thing a model learns is only to continue text . Nothing in that training says "when someone asks you a question, answer it". An early model given "What is the capital of France?" may continue with more quiz questions, because quiz questions often follow quiz questions.
Everything you like about an assistant (answering, refusing, stopping at the right place, being polite) is added by later training. There are four stages:
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1. Pretraining
predict the next token
on huge raw text
base model
2. Supervised
fine-tuning
learn from written
example answers
follows instructions
3. Preference
tuning
prefer what humans
chose (RLHF, DPO)
helpful, safer
4. Reasoning
training
reward correct final
answers (verifiable)
thinks, then replies
Most of the compute and the cost
Thousands of examples written or curated
Pairs: a better answer and a worse one
Used by reasoning models; extra tokens at answer time
Stages 2 and 3 together are often called post-training. Exact recipes differ between labs and are rarely published in full.
Four stages. Each starts from the result of the one before.
Pretraining
Training a model from scratch on a very large amount of raw text with one simple goal: predict the next token . No one labels anything; the text itself is the answer key. The result is a base model (also called a foundation model).
Post-training
Everything after pretraining that shapes behaviour: supervised fine-tuning, preference tuning and reasoning training. It uses far less data and compute than pretraining, but it decides how the model feels to use.
A helpful picture: pretraining is reading a huge library . Post-training is learning how to use what you read when a person asks a question.
The example code in code/mini-training shows each stage on a tiny scale, with a model that only counts. It is not a neural network, so it shows how each stage changes behaviour , not how the maths works. The next sections use it.
Check yourself
What is the single goal of pretraining?
Why does a base model not answer questions?