Editing Openai/6939b23e-87cc-8001-bdd9-9546d1143ce9 (section)

=== Assistant: Exactly — you cannot completely separate vocabulary from reasoning in the way the model actually works. ===
Exactly — you cannot completely separate vocabulary from reasoning in the way the model actually works. Here’s why, step by step:

==== - Both vocabulary and reasoning are emergent from the same network of neurons and weights. ====
* Vocabulary = simpler emergent patterns (word representations, associations).
* Reasoning = more complex patterns built on top of these basic representations.
* The neurons and weights that encode word patterns are also used in the chains of activations that perform reasoning.

: 

==== - You can minimize factual knowledge (i.e., prevent the model from having pre-memorized facts) by: - Fine-tuning on logic-heavy / synthetic datasets - Feeding all external knowledge as input (context) ====
* But you cannot remove token embeddings or “vocabulary patterns” entirely, because reasoning depends on the neurons’ ability to encode and manipulate symbols.

==== 1. Reasoning requires manipulating symbols/concepts — which are represented via the same distributed activations as vocabulary. ====
# Removing or ignoring the “vocabulary patterns” would break the basic signals neurons use to reason.
# Therefore, reasoning is inherently tied to the network’s internal representation of symbols (vocabulary).

===== : =====

If you want, I can make a visual showing how vocabulary activations form the foundation for reasoning patterns, so it’s crystal clear why they can’t be separated.

Do you want me to do that?