LLM training is the multi-stage process that transforms a neural network from random parameters into a capable language model — encompassing pretraining on massive text corpora, supervised fine-tuning on instruction-response pairs, and alignment through RLHF or DPO to produce models that are helpful, harmless, and honest.

What Is LLM Training?

• Pretraining: Self-supervised learning on trillions of tokens from internet text, books, and code.
• Supervised Fine-Tuning (SFT): Training on curated (instruction, response) pairs to teach format and helpfulness.
• Alignment (RLHF/DPO): Human preference optimization to make outputs safe and useful.
• Scale: Modern models train on 1-15 trillion tokens with billions of parameters.

Training Phases

Phase 1 — Pretraining:

• Objective: Next-token prediction (causal language modeling).
• Data: Common Crawl, Wikipedia, GitHub, books, scientific papers.
• Compute: 10,000+ GPUs running for weeks to months.
• Cost: $10M–$100M+ for frontier models.
• Output: Base model with broad knowledge but no instruction-following ability.

Phase 2 — Supervised Fine-Tuning (SFT):

• Data: 10K–1M high-quality (prompt, response) examples.
• Effect: Teaches the model to follow instructions and respond in desired format.
• Duration: Hours to days on 8-64 GPUs.
• Techniques: Full fine-tuning, LoRA, QLoRA for efficiency.

Phase 3 — Alignment:

• RLHF: Train reward model on human preferences, then optimize policy with PPO.
• DPO: Direct preference optimization without separate reward model.
• Constitutional AI: Self-critique and revision based on principles.
• Goal: Helpful, harmless, honest responses.

Key Concepts

• Tokenization: BPE, WordPiece, or SentencePiece converts text to tokens.
• Scaling Laws: Performance scales predictably with compute, data, and parameters.
• Distributed Training: Data parallelism, tensor parallelism, pipeline parallelism across GPU clusters.
• Mixed Precision: FP16/BF16 training with FP32 master weights for efficiency.
• Gradient Checkpointing: Trade compute for memory to train larger models.

Training Infrastructure

• Hardware: NVIDIA H100/A100 clusters, Google TPU v5, AMD MI300X.
• Frameworks: PyTorch + DeepSpeed, Megatron-LM, JAX + T5X.
• Orchestration: Slurm, Kubernetes for cluster management.
• Storage: High-throughput distributed filesystems (Lustre, GPFS).

LLM training is the foundation of modern AI capabilities — the careful orchestration of pretraining, fine-tuning, and alignment determines whether a model becomes a useful assistant or generates harmful content.