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Concept Fable

Pretraining

Conclusion first:Pre-training is the large-scale initial training phase of a model, typically learning language, knowledge and general patterns from massive corpora through self-supervised objectives. It gives the model its foundation, but it does not automatically understand a given company's processes, boundaries and tools.

Pretraining · Pretraining

Read the story first. There is no rush to give a definition here; let the question grow on its own first.

Fable

The great academy's ten-thousand-volume morning reading

The mountain academy wanted to train students who could work in various places. The old dean used to teach only counter talk: how to ask when meeting a customer, how to write when taking money, and how to respond to complaints.

The student performed well in the local shops but faltered as soon as he went to another province. The tea house asked about mountain transport, the apothecary asked about herb interactions, the accountant asked about old contract clauses, and the student could only apply counter scripts—answering neatly but without understanding the context. The dean first made the script booklet thicker. The new booklet covered more scenarios, but the more he memorized, the more rigid he became. If a question was rephrased slightly, the student couldn't find the corresponding page.

An elder suggested not rushing to teach specific tasks. Let the students spend years reading widely: geography, accounting, medicine, law, farming seasons, letters, and trade stories. The goal was not immediate readiness for a post, but learning language, common sense, and how things relate to one another.

For the first few months, the owners complained it was too slow. Later, when the same group of students entered shops again, they needed only a few rules to adapt: at the tea house they learned the tea house's taboos, at the accounts office the accounts office's formats, at the pharmacy the pharmacy's boundaries.

When handling a dispute, a student had never seen that document, yet deduced the key issue from old legal precedents and common knowledge of trade routes: the delay in goods was not merely a late arrival, but also involved the obligation to notify about mountain floods.

The director at first thought the problem was that the students were not diligent enough, so he added thirty more counter Q&As. The students memorized them more thoroughly and answered more plausibly, but whenever they encountered an unfamiliar transaction, they still described tea transport as medicinal storage and lease disputes as return-and-apology. The answers were like a skilled shell with no world inside.

Later the academy changed its three-year curriculum. In the first year, students did not take counter work, only reading geography, account books, farming seasons, contracts, medical cases, and merchants' letters; in the second year, students sat in at different shops; only in the third year did they practice specific responses. When they finally stood at the counter, although they still had to learn shop rules, they already knew what kind of common sense lay behind a single question.

Only then did the dean divide the curriculum into two stages: first build a broad foundation so students have transferable understanding; then train behavior with specific assignments. Without the first stage, the second is just memorizing answers.

Reveal

This story is about: pretraining

Pre-training is the large-scale initial training phase of a model, typically learning language, knowledge and general patterns from massive corpora through self-supervised objectives. It gives the model its foundation, but it does not automatically understand a given company's processes, boundaries and tools.

It can be understood as the apprentice first reading ten thousand books to gain language and common-sense ability; afterward, fine-tuning, alignment, evaluation, or tool integration is still needed to work stably in a specific business.

Metaphor mapping

  • Ten thousand scrolls = massive unlabeled corpus
  • Predict the next sentence or fill in the blank = self-supervised objective
  • Apprentice foundation = general capabilities of the base model
  • A certain shop's rules = subsequent fine-tuning, alignment, and tool integration

Andy Ren Xu's judgment

The practical value of this concept is that it helps you break "AI features that look smart" into deliverable, verifiable, and governable units of work.

foundation modelself-supervised learningcorpusnext-token prediction
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