Pretraining

As explained in Build a Reasoning Model (From Scratch)

The initial training stage in which a language model learns general statistical structure from large amounts of data before task-specific adaptation.

Pretraining is the initial large-scale stage of language-model development. It trains a randomly initialized model on massive unlabeled text to predict the next token, building general language patterns, knowledge, and capabilities before later adaptation. section 1.2 section 6.1

A typical pretraining workflow includes:

• Collect massive text corpora, potentially spanning terabytes of data and trillions of tokens. section 1.2
• Split text into tokens, which may be words, word parts, or punctuation, then convert them into numerical IDs. section 1.2
• Train the model to predict each next token from preceding context. section 1.2 section 1.4
• Continue with post-training stages such as instruction fine-tuning and preference tuning when better instruction following or alignment is required. section 1.2 section 1.8

Figure 1.3 Overview of a typical LLM training pipeline. The process begins with a model initialized with random weights, followed by pretraining on large-scale text data to learn language patterns by predicting the next token. Post-training then refines the model through instruction fine-tuning and preference fine-tuning, enabling the LLM to better follow human instructions and align with human preferences.

Follow the pipeline from random model initialization through large-scale next-token prediction and into instruction and preference tuning. section 1.2

Pattern matching during pretraining

Given “The capital of Germany is…”, a pretrained model generates “Berlin” by recalling a strong statistical association learned from text rather than applying explicit logical rules. The same mechanism produces fluent continuations but does not guarantee formal inference. section 1.4

How Build a Reasoning Model (From Scratch) covers it

How deep it goes

Explains the data, tokenization, next-token objective, scale, and pipeline placement, but does not detail optimization internals.

Examples

Uses tokenized sentences, the Germany–Berlin association, and a staged training-pipeline diagram.

What it assumes

Requires only cursory familiarity with language models; treats foundational pretraining knowledge as helpful but not mandatory.

What you can do after

Evaluate/understand how next-token prediction builds language capabilities and why later training changes instruction following and reasoning behavior.

How it connects

Connects pretraining with tokenization, next-token prediction, post-training, instruction fine-tuning, preference tuning, reinforcement learning, and pattern matching.

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  Build a Large Language Model (From Scratch)The initial training phase in which a language model learns general patterns from a large text corpus before task-specific adaptation.
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  Time Series Forecasting Using Foundation ModelsPretraining is initial training of a model on a large dataset to learn general patterns and relationships before applying it to specific tasks.
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  Inside Deep Learning: Math, Algorithms, ModelsA strategy of training a model on a source dataset before adapting it to a downstream task.
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  The Art of AI Product DevelopmentPretraining is the initial training of a model on broad data before adapting it to specific tasks or domains.
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  How Large Language Models WorkPretraining is the initial training of a model on broad data before adapting it to particular tasks or applications.
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  Introduction to Generative AI, Second EditionThe initial training phase in which a model learns general representations or predictive behavior from a large corpus before task-specific adaptation.
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  LLMs in ProductionPretraining is the initial training of a model on a large, broad dataset before adapting it to a specific task or domain.
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  Sutskever's ListPretraining is the initial training of a model on a broad dataset before task-specific adaptation or evaluation.
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  Data Without LabelsPretraining is an initial model-training stage that learns general patterns or representations before adaptation to a specific task.
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  Build a Website with ChatGPTPretraining is the initial training of a model on large amounts of data to learn general patterns before task-specific use.
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  Reinforcement Learning from Human FeedbackPretraining is the initial large-scale optimization of a model on broad data, commonly through next-token prediction.

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Related concepts

• foundational modelproduces · A base or foundation model is a pretrained model that provides learned parameters or capabilities as a starting point for reuse or adaptation to downstream tasks.
• pretrained modelproduces · A pretrained model is a machine-learning model whose parameters were learned through prior training before it is used or adapted for a subsequent task.
• next-token predictionuses · Next-token prediction is the task or training objective of estimating the token that follows a sequence, given its preceding tokens and any conditioning information.
• datasetuses · A dataset is an organized collection of related data items, records, or observations treated as a unit for storage, processing, analysis, or computation.
• zero-shot learningused by · Zero-shot learning is a setting in which a model performs a task or predicts categories without task-specific examples during training, adaptation, or inference, relying on learned knowledge and available task information.
• similarity searchused by · Similarity search is a retrieval technique that finds or ranks items by comparing their representations with a query according to a similarity or distance measure.

This explainer was generated by Manning's knowledge graph from the text of Build a Reasoning Model (From Scratch). The section links point to the parts of the book it draws on.