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

=== Assistant: Ah — now we’re in an interesting design trade-off. ===
Ah — now we’re in an interesting design trade-off. If your goal is maximal reasoning with a small vocabulary, the “best” AI isn’t necessarily the largest model, but one optimized for efficient internal processing. Let me break it down.

==== - Vocabulary size: the number of distinct tokens / embeddings the model uses. - Large vocabulary → better handling of rare words, nuances, multilingual input - Small vocabulary → simpler, faster tokenization, less “wasted capacity” on rare tokens ====
* Reasoning: how many ideas the model can process and relate at once - Determined by layers, hidden size, attention heads, and training style, not vocabulary.

: 

==== - You want models designed for reasoning / step-by-step thinking, not just raw knowledge. ====
* Characteristics to look for: 1. Medium-to-large hidden size relative to total vocabulary - More neurons per layer → better idea processing 2. Many attention heads / layers - Can relate multiple tokens/ideas simultaneously 3. Trained on reasoning-heavy datasets - Chain-of-thought, instruction-following, logic tasks 4. Compact vocabulary - Avoids “wasting attention” on rare/unused words
* Examples of reasoning-optimized LLMs: - MPT-7B-Instruct (Meta’s MosaicML) → good reasoning on smaller contexts, token-efficient - LLaMA 2 / 3 variants (smaller vocab versions) → can perform step-by-step reasoning - Vicuna / WizardLM (7B) → instruction-tuned, efficient token use, good reasoning

: 

==== 1. Use instruction-tuned models → reasoning and chain-of-thought are embedded in training. ====
# Prune vocabulary carefully → keep frequent / domain-specific tokens.
# Increase hidden size / attention heads if possible → reasoning capacity improves even with smaller vocabulary.
# Feed structured prompts → the smaller vocabulary won’t hinder reasoning if instructions are clear.

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If you want, I can make a diagram showing the trade-off: vocabulary size vs reasoning capacity, and how to optimize a small model for maximal reasoning efficiency.

Do you want me to do that?