Understanding the GPT-Style Assistant Training Pipeline

Large language models (LLMs) such as GPT-2, GPT-3, and ChatGPT are not trained in one step. Instead, they go through a multi-stage pipeline that gradually transforms a raw text predictor into a helpful, safe, and instruction-following assistant.

1. Pretraining

The journey starts with pretraining — teaching a neural network general language understanding.
A vast corpus of hundreds of billions to trillions of tokens is collected and carefully filtered from sources like web pages, books, encyclopedias, and open-source code. The model is trained with the causal language modeling objective: predicting the next token given the previous sequence.

This process is extremely compute-intensive, requiring thousands of GPUs or TPUs for weeks to months. The result is a base model — strong at generating text but not yet fine-tuned for safe, user-friendly responses.

2. Supervised Fine-Tuning (SFT)

Next comes supervised fine-tuning, where human annotators create instruction–response pairs (from about 10k up to a few hundred thousand examples). The base model is fine-tuned on this smaller, high-quality dataset using the same next-token prediction loss but at a far lower compute cost (often tens of GPUs for a few days).
This step yields an instruction-tuned model — better at following prompts and producing clear, task-oriented answers (e.g., InstructGPT, Vicuna).

3. Reward Modeling (RM)

To align with human judgment, developers train a reward model. Human raters compare multiple outputs for the same prompt and rank them by helpfulness and safety (typically 50k–300k comparisons).
The reward model learns to predict a preference score and is used later to guide optimization. This step requires moderate compute but is crucial for aligning outputs with what humans consider good.

4. Reinforcement Learning from Human Feedback (RLHF)

Finally, RLHF applies reinforcement learning — usually Proximal Policy Optimization (PPO) — to refine the assistant. The model is rewarded when its responses score highly on the reward model, while ensuring stability and avoiding catastrophic drift.
This makes the assistant more truthful, harmless, and helpful, bridging the gap between raw text generation and safe deployment (key for ChatGPT-like behavior).

Deployment & Continuous Improvement

After RLHF, the model can be deployed as an API or integrated into apps. User feedback — ratings, refusal cases, jailbreak attempts — is used to retrain or adjust fine-tuning and safety filters. This iterative improvement loop keeps the assistant aligned with evolving use cases and safety standards.

Glossary of Key Technical Terms

Term	Definition
LLM (Large Language Model)	Neural network with hundreds of millions to trillions of parameters trained on large text corpora.
Token	Smallest unit of text (word, subword, or character) used during training and inference.
Base Model	LLM trained only on next-token prediction without special safety or task tuning.
Instruction–Response Pair	Prompt and ideal answer used for fine-tuning.
Supervised Fine-Tuning (SFT)	Training on curated instruction data to make the model assistant-like.
Reward Model (RM)	Model predicting preference scores from human ratings to guide RLHF.
Reinforcement Learning from Human Feedback (RLHF)	RL method optimizing the model using human-derived reward signals.
PPO (Proximal Policy Optimization)	Popular reinforcement learning algorithm for fine-tuning language models.
GPU/TPU	Specialized hardware (Graphics/ Tensor Processing Units) for large-scale neural network training.
Causal Language Modeling	Objective where the model predicts the next token from the sequence so far.

If you’re exploring LLM development or safety research, understanding these stages helps demystify how cutting-edge assistants evolve from raw text predictors to human-aligned AI systems.