How LLMs Learn: The 4 Stages of Training from Scratch

Training a large language model (LLM) from scratch is a multi-stage process that transforms a blank neural slate into a powerful reasoning engine. Avi Chawla breaks it down into four essential phases:

0️⃣ Random Initialization

At the start, the model knows nothing. Its weights are random, and its responses are gibberish. It’s like asking a newborn to explain quantum physics—no data, no understanding.

1️⃣ Pre-training

This stage teaches the model the fundamentals of language. It’s trained on massive corpora to predict the next token in a sequence.

• Learns grammar, syntax, and world facts

• Still lacks conversational ability—it just continues text

2️⃣ Instruction Fine-tuning

To make the model useful, it’s trained on instruction-response pairs.

• Learns to follow prompts and format answers

• Gains abilities like summarization, coding, and Q&A

• Uses curated datasets with human-labeled instructions

3️⃣ Preference Fine-tuning (RLHF)

Here, human feedback is used to align the model’s behavior.

• Users choose preferred responses

• A reward model is trained to predict human preferences

• The LLM is updated using Reinforcement Learning (PPO algorithm)

• Helps the model respond in a way that feels natural and helpful

4️⃣ Reasoning Fine-tuning

For tasks like math or logic, correctness—not preference—is key.

• The model’s output is compared to a known correct answer

• Rewards are based on accuracy

• This is called Reinforcement Learning with Verifiable Rewards

• GRPO by DeepSeek is a leading technique here

🧭 Final Thoughts:

Training an LLM is more than just feeding it data—it’s a layered process of teaching, aligning, and refining. From raw text prediction to nuanced reasoning, each stage builds on the last to create models that can truly understand and assist.