[ExI] How AI understands the world

[BillK]

On Sat, 21 Feb 2026 at 17:49, Jason Resch via extropy-chat <
extropy-chat at lists.extropy.org> wrote:

> Manual refinement of LLM chat bots we interact with is common, but it's
> not necessary to build an LLM that understands and reasons.
>
> There is the "Pure Transformer" that is produced from simply training on a
> large sample of text, and this requires no labeling, or manual adjustments.
> GPT-2 and GPT-3 were examples of pure transformers. There was also a pure
> GPT-4 that was never made public, before it was given constraints around
> what things it can or can't do. What is interesting is that the
> intelligence of this pure model was measured to be significantly higher
> before it was put through this manual adjustment (we might liken it to
> being lobotomized).
>
> This is a generally recognized phenomenon:
> https://share.google/aimode/Xz0ejYy73wOt5nQEc
> "In summary, while RLHF might "lobotomize" certain creative or reasoning
> edges of a base model, it is currently the industry standard for making AI
> usable and safe for the general public."
>
> DeepMind encountered a similar phenomenon, when they observed that their
> Go model when pre-trained initially on records of human games, produced a
> less skilled player than a model trained on *zero* human inputs (hence
> the name "AlphaZero").
>
> So I suppose my overall point is that while "granular labor of human
> annotators" is common, it's unnecessary for an AI to develop of meaning and
> understanding.
>
> Jason
>
>> _______________________________________________



Gemini says that these Pure Transformers were never released because they
were unusable.
Labelling and manual adjustments are essential to LLM development.
BillK

Question -
It was suggested that there are some Pure LLMs that were trained only on
large text samples, with no labeling or human adjustments. Is this true?

Gemini 3.1 Pro -


Yes, this is absolutely true. In the AI industry, these are referred
to as *"Base
Models"* or *"Foundation Models."* When researchers build an AI, the
training happens in distinct stages. The models you interact with daily
(like me) have gone through multiple stages of human-guided refinement. But
the *very first stage* creates exactly what you described: a "pure"
language model.

Here is the reality of what these pure models are, how they behave, and why
you don't usually get to interact with them.
------------------------------
1. What is a "Pure" Base Model?

A base model is an AI that has been trained on one single, mathematically
simple objective: *Next-Token Prediction*.

Researchers take a massive, raw dataset (a scrape of the internet,
Wikipedia, Reddit, books, code repositories) and feed it to the algorithm. The
AI's only job is to look at a sequence of words and guess what the next
word should be.
+1


   -

   There are *no humans grading the answers*.
   -

   There are *no "Helpful, Honest, Harmless" guidelines*.
   -

   There is *no concept of a conversation*.

*Examples of Base Models:* The original GPT-3 (released in 2020), the base
versions of Meta's LLaMA (e.g., Llama-3-8B, not the "Instruct" version),
and Mistral's base models.

2. How Do They Behave? (The "Alien" Phase)

If you were to sit down at a terminal and type to a pure base model, it
would not act like a helpful assistant. It acts like an obsessive
autocomplete engine.

   -

   *The "Question" Problem:* If you prompt a base model with: *"What is the
   capital of France?"*, it likely will not answer *"The capital of France
   is Paris."* Instead, because it learned from internet forums and school
   quizzes, it might output: *"What is the capital of Germany? What is the
   capital of Italy? What is the capital of Spain?"* It just continues the
   pattern.
   -

   *The "Stochastic Parrot":* Because they are just statistical mirrors of
   the raw internet, pure base models are deeply chaotic. They will readily
   generate biased, toxic, rambling, or completely nonsensical text if the
   prompt pushes them in that direction.

3. The "Socialization" Phase (Why Humans Step In)

Because base models are commercially useless to the average consumer,
companies have to "tame" them. This is where the massive human workforce we
discussed earlier steps in.

They take the pure base model and put it through *Supervised Fine-Tuning
(SFT)* and *Reinforcement Learning from Human Feedback (RLHF)*. This forces
the model to stop acting like an internet-autocomplete engine and start
acting like a polite, conversational agent that answers questions directly
and refuses harmful requests.

4. The "Pure" Caveat

While it is true that no humans are grading the AI's outputs during the
base training phase, it is slightly inaccurate to say there is *zero* human
adjustment.

   -

   Humans still choose *what data goes into the training set*.
   -

   Engineers write scripts to filter out certain websites, remove duplicate
   data, or scrub personal information.

   So, while the *training mechanism* is purely autonomous, the
   *environment* the AI learns in is still curated by humans.


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