How are LLMs trained?

How Are Large Language Models (LLMs) Trained?

Training Large Language Models is a complex, multi-stage process that transforms raw text data into sophisticated AI systems capable of human-like conversation. Here's a detailed look at how companies like OpenAI, Google, and Anthropic create these powerful models.

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The 5 Key Stages of LLM Training

1. Data Collection and Preparation

LLMs require massive datasets - typically terabytes of text from diverse sources:

• Books (fiction and non-fiction)
• Scientific papers and journals
• News articles
• Wikipedia and other reference sites
• Publicly available code repositories
• Forum discussions (with careful filtering)

This data undergoes rigorous cleaning and filtering to remove low-quality content, duplicates, and potentially harmful material.

2. Tokenization

Before training begins, text is converted into tokens - numerical representations of words or word parts:

• Common words become single tokens ("cat" = 1234)
• Rare words are split into subword units ("unhappiness" → "un", "happiness")
• Special tokens mark sentence boundaries and other structural elements

Modern LLMs typically use between 50,000-250,000 unique tokens in their vocabulary.

The Training Process Visualized

1. Input: "The cat sat on the..."
2. Model predicts: "mat" (with 75% probability)
3. Actual next word: "couch"
4. Adjustment: Model updates its parameters to better predict similar sequences
5. Repeat: Billions of times across trillions of examples

3. Pretraining (The Core Phase)

This is where the model learns fundamental language understanding through self-supervised learning:

• Masked language modeling: Predict missing words in sentences
• Next-token prediction: Guess what word comes next in sequences
• Massive computing: Uses thousands of GPUs/TPUs for weeks or months

The model adjusts its parameters (weights) through backpropagation to minimize prediction errors.

4. Fine-Tuning

After pretraining, models undergo specialized refinement:

Fine-Tuning Method	Purpose	Example
Supervised Fine-Tuning	Improve specific capabilities	Teaching coding through Python examples
Reinforcement Learning (RLHF)	Align with human preferences	Making responses more helpful and harmless
Domain Adaptation	Specialize for particular fields	Medical or legal applications

RLHF (Reinforcement Learning from Human Feedback) is particularly important for making models useful and safe, as described in this research paper.

5. Evaluation and Deployment

Before release, models undergo rigorous testing:

• Benchmark testing: Standardized tests for accuracy, reasoning, etc.
• Safety evaluations: Checking for biases, harmful outputs
• Real-world testing: Limited release to gather user feedback

Only after passing these checks is the model deployed via APIs or consumer products.

The Hardware Behind LLM Training

Training modern LLMs requires enormous computational resources:

Resource	Typical Requirement	Example
GPUs/TPUs	Thousands working in parallel	NVIDIA A100 or H100 clusters
Training Time	Weeks to months	GPT-4 took ~100 days
Energy Consumption	Equivalent to small town	~1,300 MWh for GPT-3
Memory	Terabytes of VRAM	Specialized high-bandwidth memory

Key Challenges in LLM Training

Major Training Obstacles

• Data quality: Finding enough high-quality, diverse text
• Computational costs: Millions in hardware and energy
• Overfitting: Remembering data instead of learning patterns
• Catastrophic forgetting: Losing old skills when learning new ones
• Alignment: Making models behave as intended

Recent Advances in Training Techniques

Researchers continue developing more efficient methods:

• Mixture of Experts: Only parts of model activate per input
• Quantization: Using lower-precision numbers to save memory
• Curriculum learning: Starting with simpler examples
• Sparse attention: Focusing on most relevant text parts

These innovations help reduce the environmental impact and cost of training while maintaining model quality.

The views and opinions expressed in this article are based on my own research, experience, and understanding of artificial intelligence. This content is intended for informational purposes only and should not be taken as technical, legal, or professional advice. Readers are encouraged to explore multiple sources and consult with experts before making decisions related to AI technology or its applications.