By skyforbes 
Why aligned AI feels safer and more helpful than raw models
LLMs are capable of generating code to advising legal strategy but why do the raw, foundational models (like a pure GPT-3 trained only on internet data) often produce unhelpful, or incoherent outputs, while polished versions (like ChatGPT or Claude) are polite, cautious, and accurate?
The answer lies in the transformative process known as RLHF: Reinforcement Learning from Human Feedback. RLHF is not about teaching the model new facts; it’s about teaching the model human values, safety, and etiquette. It is the critical step that aligns the model with our intentions.
The Problem RLHF Solves: The “Imitation Game” Flaw
The initial stage of LLM training (Pre-training) teaches the model to be an outstanding predictor of the next word. It learns grammar, style, and world knowledge by imitating the entire internet.
— Problem: The internet contains misinformation, bias, toxicity, and conflicting instructions. If an LLM simply imitates everything it sees, it becomes a mirror reflecting the worst parts of its data. It doesn’t know the difference between a truthful answer and a convincing lie, or a helpful suggestion and a dangerous…