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Pre-training Process

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The Pre-training Journey

Pre-training is the process of teaching an LLM to predict the next token by exposing it to vast amounts of text data.

Key Stages of Pre-training

  1. Initialize the Model

    Start with random weights across all layers

  2. Batch Processing

    Process chunks of text from the corpus in parallel

  3. Forward Pass

    Generate predictions for each token position

  4. Calculate Loss

    Measure prediction errors across the vocabulary

  5. Backward Pass

    Compute gradients to adjust model weights

  6. Update Weights

    Improve the model using optimization algorithms

  7. Repeat at Scale

    Continue until convergence or resource limits

Training Dynamics

Learning Progression

Training Tokens Loss Basic patterns Language structure Knowledge
Loss decreases as model learns patterns

Progressive Learning

LLMs learn in stages: simple patterns first, then grammar, facts, and eventually more complex reasoning.

Emergence

Complex capabilities like reasoning and problem-solving emerge only after sufficient scale in data and model size.

Scaling Laws

Performance improves predictably as compute, data, and model size increase together.

Pre-training at Scale

Computing Infrastructure

Resource Scale
GPUs/TPUs Thousands of accelerators
Duration Weeks to months
Power Megawatts of electricity
Cost $1-100+ million USD
Technical Challenges
  • Distributed training across thousands of devices
  • Memory optimization for large models
  • Checkpoint management for failure recovery
  • Data pipeline optimization
  • Training stability at scale

Training Optimization

Mixed Precision Training

Using lower precision (FP16/BF16) with careful handling of numerical stability

Gradient Accumulation

Compute gradients in smaller batches and update after multiple steps

Distributed Sharding

Split model parameters, gradients, and optimizer states across devices

Learning Rate Scheduling

Carefully control learning rates over training time for stability

A single training run crash can cost millions of dollars, making stability essential.

The Scaling Hypothesis

"Many capabilities of large language models emerge naturally as we scale up training, without specific training objectives or architectural changes."

- Based on observations from GPT-3 and subsequent models

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