LLM Pre-training

Definition of Pre-training

Pre-training is the process of training a model on vast, diverse, and largely unlabeled data to learn general representations and patterns of a domain — such as language, vision, audio, or sensor data — before it is specialized for specific tasks.

It is a self-supervised learning stage where the model develops an internal “world model” by predicting or reconstructing parts of its input (e.g., the next token, masked pixel, next audio frame, or next action).
The goal is not to perform a narrow task, but to build a foundation of understanding that later fine-tuning, prompting, or reinforcement can adapt to many downstream objectives.

---

🧠 Core Idea

Pre-training = learning how the world looks, sounds, or reads —
before learning how to do something with that knowledge.

General Formulation

Aspect	Description
Input	Large, diverse, unlabeled data (text, images, audio, code, trajectories, etc.)
Objective	Predict missing or future parts of data (self-supervised task)
Outcome	Dense, structured representations (embeddings) capturing meaning and relationships
Purpose	Build transferable understanding to accelerate later adaptation

Why It Matters

Pre-training converts raw data → reusable intelligence.
Once the base model is pretrained, it can be:

• Fine-tuned for specialized tasks,

• Aligned for human intent (via RLHF),

• Connected to live knowledge (via RAG).

It’s the difference between:

teaching a brain how to think and perceive,
versus teaching it what to think or do.

When You Should Pre-train

You should pre-train from scratch only when:

• You need a new base model (new architecture, tokenizer, or modality);

• Existing models don’t cover your language or data type (e.g., low-resource languages, medical imaging, genomic data);

• You want full control over knowledge, bias, and compliance;

• You’re performing foundational research into architectures or training dynamics.

Otherwise — reuse and fine-tune an existing pre-trained foundation.