Introduction
ChatGPT is not a single-stage model—it’s the product of a carefully designed, multi-phase training pipeline. Each phase improves upon the last to transform a raw language model into an intelligent conversational assistant.
The three main stages of ChatGPT’s training are:
- Generative Pre-Training (Self-Supervised Learning)
- Supervised Fine-Tuning (SFT)
- Reinforcement Learning with Human Feedback (RLHF)
This tutorial will describe each stage, explain why it is necessary, and highlight the challenges and lessons from OpenAI’s approach.
1. Model Genesis
Before ChatGPT, OpenAI developed InstructGPT, which focused on making GPT models follow instructions accurately. InstructGPT showed that fine-tuning a model with human feedback could make it more helpful, honest, and safe.
ChatGPT extends this approach by making the model capable of multi-turn conversations, maintaining memory of earlier messages, and responding more naturally. As R. Pradeep Menon explains, “InstructGPT was originally meant to be all about following instructions, but ChatGPT takes that idea and kicks it up a notch.”
The evolution can be summarized as:
Base GPT Model → InstructGPT (Instruction Tuning) → ChatGPT (Conversational Fine-Tuning)
Each stage refines the model’s alignment with human expectations.
2. Stage 1 – Generative Pre-Training
What Happens
In this stage, the model learns from massive amounts of text data gathered from books, articles, web pages, and other public sources. The model’s task is simple: predict the next word in a sentence based on the previous words.
This is called self-supervised learning because the model doesn’t require labeled data—every next word acts as its own label.
The outcome is a model that develops a deep understanding of language: grammar, semantics, and even factual knowledge.
Why It Matters
This step provides the model with its general intelligence. It becomes fluent in language and gains a broad awareness of the world. However, it doesn’t yet understand what humans want it to do. It simply learns how to continue text sequences.
As the article points out, “There is a misalignment in expectations. The model can handle a lot of different tasks, but it’s not trained for any particular one.”
Key Limitation
Although the model becomes good at generating coherent sentences, it might still produce irrelevant or misleading text because it lacks goal-directed behaviour.
Text Diagram
[Large Text Corpus] → Pre-Training → Base GPT Model (General Language Understanding)
3. Stage 2 – Supervised Fine-Tuning (SFT)
What Happens
After pre-training, the model is fine-tuned using human-crafted examples of conversations. Teams of annotators simulate real user interactions: one person plays the “user,” and another plays the “assistant.”
These dialogues form training pairs, where the model learns to respond the way a helpful assistant would. During this supervised training, the model adjusts its internal parameters (weights and biases) to match the example responses.
As the article describes, “Supervised Fine-Tuning is a three-step process: create crafted conversations, make a training corpus, and train using stochastic gradient descent.”
Why It’s Needed
This stage teaches the model how to behave in a conversational setting. Instead of merely predicting the next word, it learns to provide relevant, context-aware answers that follow instructions naturally.
The supervised fine-tuning stage aligns the model’s responses with the desired conversational style—helpful, factual, and user-friendly.
Remaining Problem
Even after SFT, there’s an issue called distributional shift—the difference between the curated training data and the diverse, unpredictable prompts from real users. As Menon explains, “The model creates an expert policy based on the conversations SFT used to train it,” meaning it might not generalize perfectly to every new scenario.
Text Diagram
Base GPT Model → Fine-Tuning with Human Dialogue → SFT Model (Conversationally Trained)
4. Stage 3 – Reinforcement Learning with Human Feedback (RLHF)
What Happens
This stage is the heart of ChatGPT’s alignment process. It combines machine learning and human judgement to optimize the model for human preferences rather than mere word prediction.
Here’s the step-by-step process:
- Generate Responses:
The SFT model is used to generate several possible answers for each user prompt. - Human Ranking:
Human reviewers rank the responses from best to worst, based on usefulness, accuracy, clarity, and safety. - Train a Reward Model:
A separate model (called the reward model) learns to predict which response humans would prefer. It assigns a numerical score to each response. - Reinforcement Learning (PPO):
Using the reward model as a guide, the system fine-tunes the main model again with reinforcement learning algorithms. The goal is to maximize the reward score—i.e., to produce outputs that humans find best. - KL-Divergence Regularization:
To prevent the model from drifting too far from its original conversational style, a regularization technique keeps the new model close to the SFT version.
Why It’s Important
This stage moves the model from human-imitating to human-aligned. It learns not only to respond like people do, but also to respond the way people want it to.
As the article explains, “The reward function is basically a way of turning our goals into a number.” This allows the AI to be tuned in a measurable, repeatable way.
Ongoing Challenges
Even with RLHF, there are persistent challenges:
- Reward Model Imperfections: Human feedback is subjective and inconsistent.
- Goodhart’s Law: If the model optimizes too hard for the reward function, it may “game” the system—producing responses that score high but are not truly helpful.
- Balancing Creativity and Safety: The model must avoid both overly cautious and overly risky responses.
Text Diagram
Prompt + Context ↓ SFT Model generates multiple responses ↓ Humans rank responses ↓ Reward Model learns human preferences ↓ Reinforcement Learning (PPO) ↓ Final ChatGPT Model (Human-Aligned)
5. Key Challenges in ChatGPT’s Training
1. Misalignment
The base model is optimized for next-word prediction, but humans expect meaningful, truthful, and contextually aware answers. Bridging this gap requires multiple tuning stages.
2. Distributional Shift
When faced with real-world user prompts that differ from training examples, the model can behave unpredictably.
3. Reward Hacking
When a system is optimized for a particular metric (the reward score), it may find shortcuts that meet the metric but miss the intent—this is Goodhart’s Law in action.
4. Cost and Scale
Training involves billions of parameters and thousands of GPUs running for weeks. Collecting and ranking human feedback is also expensive.
5. Human Dependence
Both SFT and RLHF rely heavily on humans for labeling, conversation crafting, and ranking, which introduces biases and limitations.
6. Complete Training Flow
Here’s a high-level view of the complete process:
Large Text Corpus ↓ Pre-Training → Base GPT Model ↓ Supervised Fine-Tuning → SFT Model ↓ Reinforcement Learning with Human Feedback → Final ChatGPT
At each step, the model evolves from a general language generator to a context-sensitive assistant.
7. Key Takeaways
- Pre-Training builds knowledge.
It equips the model with a general understanding of language and the world. - Fine-Tuning teaches behaviour.
The SFT stage makes the model more conversational and task-focused. - Reinforcement Learning aligns intentions.
RLHF ensures that the model not only responds fluently but also in ways humans prefer. - Human feedback is essential.
Without humans ranking and guiding, the model cannot learn what “good” or “helpful” means. - Training is iterative.
New versions of ChatGPT are continuously retrained with updated data, feedback, and safety mechanisms.
8. Conclusion
ChatGPT’s training process represents a breakthrough in aligning machine learning with human expectations. From its massive pre-training on internet text to its fine-tuning with human conversation and reward-based optimization, ChatGPT demonstrates how a model can evolve from predicting text to understanding intent.