AI 究竟怎样从人类反馈中学到东西？By What Means Does AI Pick Things Up From Human Feedback?

Watch a disordered text predictor turn into an assistant that is both helpful and harmless: here is how RLHF, DPO and the unseen human labour force actually school machines in 2026.

Ask one of today's assistants for a poem, help untangling tangled code, or a tour of quantum physics, and the answer comes back sounding strikingly human — courteous, neatly arranged, useful. What sits underneath, though, began life as anything but. The raw network was once a wild, unreliable generator of text; freed of guidance it might just as readily pour out toxic nonsense, biased tirades or word salad. What carries such a brute algorithm across the gap to an assistant that helps without doing harm? That crossing is the work of alignment, and the question worth asking is how the crossing is made — how, precisely, human feedback teaches an AI.

By 2026 this is no longer a question reserved for computer scientists. As the models work their way deeper into daily life, from support desks to reading medical scans, it pays to understand the training that lines them up with human values. This guide walks through what goes on under the hood of RLHF, traces the more recent DPO approach, and introduces the hidden human labour doing the teaching.

01The Trouble with Raw Models: Why Pre-Training Alone Falls Short

Lessons from feedback cannot begin until the basics of language are in place. That groundwork is laid in "pre-training," as the model sweeps through enormous stretches of the internet — books, articles, code, forums — with one narrow goal, "next-token prediction": it picks up that after the words "The sky is," the likeliest follow-up is "blue."

A serious problem follows. The internet is thick with arguments, falsehoods, abuse and gibberish, and a raw model chasing statistical odds carries no built-in feel for "truth," "helpfulness" or "safety." Ask it how to bake a cake and a flawless recipe may come back — or a made-up tale about a cake that came alive, or, worse still, something harmful copied from a dark corner of the web. End users may try to steer outputs at inference time with prompt engineering, but helpfulness and safety have to be baked into the foundational weights long before the public ever meets the model.

The answer researchers found is a multi-stage pipeline that takes a disordered text predictor and shapes it into a useful assistant, and the best-known, most widely used stage of that pipeline is RLHF.

02RLHF: The Benchmark Every Other Method Is Measured Against

Reinforcement Learning from Human Feedback (RLHF) is the breakthrough that put models such as ChatGPT within reach, joining cold statistical prediction to the subtleties of human judgement. Intricate as it is, the procedure unfolds as three clean, ordered stages.

Stage One: Supervised Fine-Tuning (SFT)

A smaller, carefully chosen set of prompts and model answers written by people is used first to fine-tune the raw model. Contractors draft examples of what a "good" exchange looks like, which gives the model the basic shape of a conversation (User: X, Assistant: Y) and a floor of competence. Even so, it is mostly copying the people who wrote those examples; the preferences underneath have not genuinely sunk in.

Stage Two: Building the Reward Model

This is the point where genuine "human feedback" enters. The SFT model is handed a prompt and told to produce a spread of answers — four distinct takes on the same question, say — and annotators read through them and arrange them from strongest to weakest against measures such as helpfulness, honesty and harmlessness.

Many thousands of such orderings go into training a separate model, the "Reward Model," with one task: guess how a human would score any answer it sees, learning to stamp good replies with a high "reward score" and poor ones with a low mark.

Stage Three: Reinforcement Learning (PPO)

The original SFT model is set loose to generate again, only now the Reward Model, rather than a person, does the marking. Reinforcement learning in plain language makes the last step easier to picture: think of a dog picking up a trick and earning a "treat" — a strong reward score — whenever it pleases the Reward Model, then re-tuning its internals to collect more treats. Under Proximal Policy Optimization (PPO), the AI slowly learns to answer in ways that keep scoring well, absorbing human preferences as its own.

03The Newer Contender: Direct Preference Optimization (DPO)

RLHF changed the field, but it carries a real weakness: holding training steady is enormously hard. A separately trained Reward Model adds layers of complication, and the PPO stage is famously temperamental; minor faults can let the AI "hack" the model, churning out strange, repetitive prose that scores brilliantly in the math and reads like rubbish to people.

Direct Preference Optimization (DPO), released in 2023 and by 2026 the choice across many leading-edge models, cuts through the tangle. A mathematical insight collapses the whole procedure: rather than train a separate Reward Model and run elaborate reinforcement learning, DPO reaches for a neat trick in the math to refresh the AI's policy straight from human preference data.

Dropping the Reward Model makes DPO quicker to train, far steadier and much harder to reward-hack. The lesson is that a heavyweight reinforcement loop is not strictly required to teach human tastes; the right mathematical framing will do.

04The People Behind the Machine: Who the Annotators Are

AI is often spoken of as though it were magic, yet RLHF and DPO alike rest entirely on very large bodies of human labour. The feedback comes from data annotators and contractors, and, more and more, from specialists in a given field.

General chatbots are scored against extraordinarily detailed rubrics covering factual correctness, tone, safety and even faint biases, but annotators drawn from the general pool no longer suffice once the models grow capable. Outputs of Python code cannot be ranked reliably by someone who cannot code, a problem closely tied to the way scientists measure how intelligent an AI is through disciplined red-teaming and adversarial tests. Companies now bring in expert annotators — chemists with PhDs, senior engineers, legal professionals — to judge specialist models at a high level.

05Judging Results: How Do We Know the Lessons Stuck?

Once RLHF or DPO training is complete, developers cannot just ask the AI whether it has grown helpful and safe; the answer will always be yes. Instead they lean on a mixture of automatic benchmarks and evaluation by people.

The model is put through sweeping tests such as the MMLU benchmark, to confirm that alignment did not blunt its core intelligence — the so-called "alignment tax" — and through "red teaming," where ethical hackers and specialists do their best to bait it into toxic output or careless disclosure. A well-judged refusal, or a graceful handling of the bait, counts as a pass.

Rigor of that kind matters because these models now run in high-stakes settings. Looking at AI's role in scientific research, we depend on systems that are strongly aligned, factual and free of hallucinations; in a laboratory, a misaligned model might suggest a dangerous mixture of chemicals or badly misread crucial data.

06What Comes Next: AI Grading AI, and Constitutional AI

Human labour is the tightest bottleneck in alignment, since engaging thousands of specialists to rank millions of answers is slow and extraordinarily costly, and the RLHF pipeline strains as models climb toward trillions of parameters. Ironically, the road forward runs through AI itself, set to work on the problems AI created.

"Constitutional AI" and "AI-assisted feedback" are now being pioneered: a highly capable model receives a short set of core principles — a "constitution" — and is told to criticise and rewrite what a weaker, unaligned model produces, standing in for the human annotator and supplying feedback at scale from its own grasp of safety and helpfulness. Letting an AI define its own values raises genuine philosophical questions, yet for the enormous next-generation models now being built, it stands as the only workable route.

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