Pretraining 101: Data, Scale, and the Loss Function

2026-06-26 · Source: MLWhiz: Recs|ML|GenAI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, short

Summary

The article "Pretraining 101: Data, Scale, and the Loss Function" explains pretraining, the initial and most expensive phase of building Large Language Models (LLMs). Pretraining involves teaching a model with random weights to predict the next token across trillions of generic text tokens, a self-supervised process where text acts as its own answer key. This stage, costing millions of dollars and months of compute, accounts for approximately 99% of an LLM's raw knowledge. The post details four critical aspects: the cross-entropy loss function, the origin and importance of 15 trillion tokens of data, the FLOP math behind multi-million dollar compute runs, and scaling laws demonstrating that smaller models with more data outperform larger models with less. It also distinguishes pretraining from post-training (instruction/preference tuning) and fine-tuning (user-specific adaptation).

Key takeaway

For AI Scientists and Machine Learning Engineers developing or deploying LLMs, understanding the distinct stages of pretraining, post-training, and fine-tuning is crucial. Recognize that pretraining is a multi-million dollar, self-supervised endeavor for base models, while your fine-tuning efforts, often parameter-efficient, adapt existing models to specific tasks. This distinction informs resource allocation and strategic decisions regarding model acquisition versus custom development.

Key insights

LLM pretraining is a self-supervised process where text inherently provides its own training labels for next-token prediction.

Principles

• Pretraining is self-supervised, using text as its own answer key.
• Smaller models trained on more data outperform larger models with less data.
• Pretraining accounts for ~99% of an LLM's raw knowledge acquisition.

Method

Pretraining involves repeatedly predicting the next token, measuring error with cross-entropy loss, and nudging model weights based on the discrepancy.

In practice

• Use a companion notebook to pretrain a tiny GPT from scratch on a laptop CPU.
• Adapt existing models using parameter-efficient fine-tuning (LoRA/PEFT) for domain-specific tasks.

Topics

• LLM Pretraining
• Self-Supervised Learning
• Cross-Entropy Loss
• Scaling Laws
• Instruction Tuning
• Parameter-Efficient Fine-Tuning

Best for: AI Student, Machine Learning Engineer, AI Scientist

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• The World Of LLMs Post Scaling Laws — AI model development has shifted from pretraining scale to post-training and verifiable data quality due to data exhaustion.
• No, Pasting Data Into ChatGPT Does Not Train It — Prompting and RAG provide temporary context; fine-tuning permanently alters an LLM's internal parameters and latent space.
• Scaling Laws for Task-Specific LLM Distillation — Chain-of-thought supervision is key to preserving general knowledge during task-specific LLM compression.
• I created an LLM trained solely on Jeffrey Epsteins emails to see how messed up it becomes :) — Fine-tuning an LLM on a controversial, unannotated dataset can reveal unique behavioral shifts and spark technical debate.

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Editorial summary, takeaway, and curation by AIssential. Original article published by MLWhiz: Recs|ML|GenAI.