Technical Principles and Training Process of ChatGPT

ChatGPT has become a hot topic in the AI community, attracting widespread attention for its impressive conversational abilities, which stem from advances in large language models (LLMs) and AIGC techniques.

The system builds on the GPT‑3.5 model and incorporates a “human‑annotated data + reinforcement learning from human feedback (RLHF)” framework to fine‑tune the pretrained model, enabling it to understand diverse user instructions and produce high‑quality, safe responses.

The training process is divided into three stages:

Stage 1 – Supervised fine‑tuning: Human annotators provide high‑quality answers for a sampled set of prompts, which are used to fine‑tune GPT‑3.5 so it can grasp user intent.

Stage 2 – Reward Model (RM) training: For each prompt, the fine‑tuned model generates multiple answers; annotators rank them, creating pair‑wise data that trains a reward model to score answer quality.

Stage 3 – Reinforcement learning (PPO): The reward model evaluates answers generated by a policy model; the resulting scores serve as rewards to update the policy via PPO, improving the LLM’s ability to produce high‑reward responses.

Iterating between stages 2 and 3 progressively enhances the model, as the RM becomes more accurate and the policy learns from higher‑quality feedback.

The article also examines whether ChatGPT could replace traditional search engines, noting three main obstacles: occasional hallucinations, difficulty incorporating new knowledge without costly retraining, and high inference costs.

Potential solutions include augmenting ChatGPT with retrieval‑based evidence display (as in DeepMind’s Sparrow) and integrating external knowledge bases, similar to Google’s LaMDA approach, to address credibility and freshness of information.

Finally, a hybrid search architecture is proposed where a conventional search engine and ChatGPT operate as dual engines—initially with the search engine as the primary source and ChatGPT as a supplementary assistant, eventually shifting to a ChatGPT‑centric model as costs decrease.