Key Takeaways
The topic in one breath
This article is about ChatGPT and the main “after pretraining” training steps that make it act like a helpful assistant: Supervised Fine-Tuning, Reinforcement Learning from Human Feedback, and Direct Preference Optimization.
Why “hybrid” keeps coming up
Hybrid AI means a system that mixes machine learning with symbolic rules or other structured methods, aiming to get both flexible pattern learning and clearer control.
A simple way to hold the ideas
Think of the model as a strong text predictor first, then a student that learns examples, then a student that learns preferences, and finally a student that learns preference choices more directly.
Story & Details
A clear name, a clear job
ChatGPT is a conversational assistant built on large language models. At its core, it predicts the next word. That is powerful, but it does not automatically make the model follow instructions, stay on topic, or match what people usually want. That is where post-training matters.
Step one: Supervised Fine-Tuning
Supervised Fine-Tuning, often shortened to SFT, is the “learn by good examples” step. People write demonstrations: questions and strong answers. The model trains on these pairs. The result is simple: the assistant style becomes more consistent, more direct, and more aligned with common instruction patterns.
Step two: Reinforcement Learning from Human Feedback
Reinforcement Learning from Human Feedback, shortened to RLHF, adds a different kind of signal. Instead of only copying examples, the model learns what humans prefer when they compare answers. A separate component, often called a reward model, learns to score answers the way human reviewers tend to score them. Then the language model is trained to produce answers that score higher.
A public university talk in April 2023 described this idea in the context of systems that power ChatGPT, including the practical problem it tries to solve: the world is full of tasks where “the right answer” is not only about facts, but also about tone, clarity, and usefulness.
Step three: Direct Preference Optimization
Direct Preference Optimization, or DPO, is a newer path that aims to use preference data more directly. In plain terms, it trains the model to favor the preferred answer over the non-preferred one, without going through the same style of reinforcement learning loop. It is still about preferences, but it can be simpler to run in practice.
Where the “hybrid AI” lens fits
Hybrid AI, in its everyday sense, is about mixing strengths. Machine learning is strong at patterns in messy data. Symbolic methods and rules are strong at structure: crisp constraints, clear logic, and stable checks. In real products, the mix can show up in many small ways: rules that block unsafe outputs, structured tools that check formats, or logic that routes a request to the right module.
In research, hybrid approaches often aim to combine language understanding and generation with explicit knowledge or reasoning structures, so the system can be both fluent and more controllable.
A tiny Dutch mini-lesson, built for real use
Dutch is used in the Netherlands (Europe). Two short phrases can help in daily life, and they also show how small building blocks can feel “rule-like” and reliable.
First, the big meaning in one easy line: “Dank je wel” is a common, friendly way to say thanks.
Now the word-by-word view with practical notes. “Dank” maps to thanks. “Je” maps to you in an informal way. “Wel” adds emphasis, like well or really, making the thanks warmer. In a shop or with a colleague, this sounds natural and polite. With close friends, it still fits. In a very formal setting, “Dank u wel” is the more formal variant, because “u” is the formal you.
Second, the big meaning in one easy line: “Kunt u dat herhalen?” is a polite way to ask someone to repeat something.
Word-by-word with tone: “Kunt” maps to can, in a polite question form. “U” is formal you. “Dat” maps to that. “Herhalen” maps to repeat. This is suitable with strangers, staff, or anyone where polite distance is expected. With friends, a common informal variant is “Kun je dat herhalen?”
What this means in practice
SFT, RLHF, and DPO are not magic words. They are training methods that help turn a raw predictor into an assistant that better follows instructions and better matches human preferences. Hybrid AI is a useful frame for thinking about control: when learning is not enough, structure can help.
Conclusions
A calm bottom line
By December 2025, the story of modern assistants is no longer only about bigger models. It is also about the training steps that shape behavior after pretraining, and about the practical blend of learning and structure that makes a system feel steady in real use.
A small takeaway to remember
Good assistants are trained not only to speak, but to respond the way people actually find helpful: clear, safe, and aligned with everyday expectations.
Selected References
[1] https://www.youtube.com/watch?v=hhiLw5Q_UFg
[2] https://eecs.berkeley.edu/research/colloquium/230419-2/
[3] https://arxiv.org/abs/2203.02155
[4] https://arxiv.org/abs/2305.18290
[5] https://rlhfbook.com/book.pdf
[6] https://arxiv.org/html/2401.11972v2
[7] https://arxiv.org/abs/1706.03762
Appendix
ChatGPT. A chat-style assistant built on a large language model that generates text by predicting likely next words, then refined to follow instructions and preferences.
Direct Preference Optimization (DPO). A method that trains a model on paired human preferences so it learns to choose the preferred answer more directly.
Hybrid AI. A system design that mixes machine learning with symbolic rules or other structured methods to combine flexibility with stronger control.
Machine Learning. A way for computers to learn patterns from data, so they can make predictions or decisions without being manually programmed for every case.
Reinforcement Learning (RL). A learning setup where an agent learns actions by trying things and receiving feedback signals that reward better outcomes.
Reinforcement Learning from Human Feedback (RLHF). A method that uses human preference judgments to train a model toward outputs people prefer, often via a learned reward signal.
Supervised Fine-Tuning (SFT). Training a model on input-output examples so it learns to produce desired answers for similar inputs.
Symbolic AI. AI methods that use explicit symbols, rules, and logic, often aiming for clear structure and predictable reasoning.
Transformer. A neural network architecture based on attention that underlies many modern language models.
My Diary - Leonardo Cardillo 