Conceptual Question: Why is a large pre-training dataset (10GB+) necessary if I only want the model to specialize in a small domain (100MB)?

[tllmmaster]

Hi Sebastian and the community,
I have a conceptual question that I am struggling to understand intuitively, and I would appreciate a clear explanation.

My Scenario:
I am training a LLM from scratch. I have a specific, clean dataset of about 100MB (in the Turkmen language).
My Goal: I only want the model to answer questions based on the information contained within this 100MB dataset. I do not expect the model to have general world knowledge, solve math problems, or know about history outside of my dataset.
My Question:
If my scope is so limited, why is training from scratch on just 100MB often considered insufficient for high-quality results?
I am trying to understand the fundamental value of pre-training on a massive dataset (e.g., 10GB or 1TB) in this context.
Does the large dataset mainly provide facts (which I don't need)?
Or does the large dataset provide the fundamental reasoning capabilities, grammar structures, and logic that a 100MB dataset simply cannot teach the model?
In other words: If I train strictly on 100MB, will the model fail to learn "how to speak/think" properly, even if it memorizes the text?
Could you please explain the difference between a model trained on 100MB vs. 10GB in terms of its internal capability to process my specific data?
Thank you for your time!

[Manoj-Gujare]

Training an LLM from scratch on 100MB of data is often insufficient because massive pre-training provides the structural "reasoning engine" of a language, rather than just a collection of facts. While your 100MB dataset contains the specific information you want the model to know, it lacks the statistical density required for a neural network to learn the complex nuances of grammar, logic, and syntax from a blank slate. In such a small corpus, many words and linguistic structures appear too infrequently for the model to build robust internal embeddings. Consequently, the model will likely resort to rote memorization; it might autocomplete a sentence it has seen before, but it will likely fail to answer a question if the phrasing differs even slightly from the training text because it hasn't learned the underlying "logic" of the language.

Furthermore, the value of a 10GB+ dataset isn't just the facts it contains, but the "connective tissue" of human thought it demonstrates—such as cause and effect, synonymy, and logical deduction. This large-scale exposure acts as a scaffolding that allows the model to "think" and "speak" coherently. Without this foundation, a model trained strictly on 100MB will have a very sparse internal map of the Turkmen language, leading to frequent hallucinations, repetitive loops, and an inability to generalize. To achieve your goal, the industry standard is to start with a pre-trained "base" model that already understands how to process language and then fine-tune it or use Retrieval-Augmented Generation (RAG) on your 100MB of data to ensure it stays within your specific scope.