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

=== Assistant: Ah! ===
Ah! Now I understand exactly — you’re asking about maximizing reasoning capability independently of the vocabulary, because you plan to feed the context yourself (lists of words, external info). So vocabulary is basically just “labels,” and your priority is the model’s pure reasoning power over a large context. Let’s break this down carefully.

==== 1. Model size / hidden layers - More neurons per layer → more capacity to represent complex relationships between ideas. ====
# Number of layers - More sequential transformations → deeper reasoning chains (like multiple passes of thought).
# Number of attention heads - Determines how many relationships between tokens the model can track simultaneously.
# Training on reasoning tasks - Instruction-tuning and chain-of-thought training make models better at: - Step-by-step deduction - Hypothetical reasoning - Multi-step problem solving
# Ability to handle long context windows - Large context window models (70k–130k tokens or more) allow you to feed huge lists or structured content. - This is crucial if you plan to add all the context externally rather than relying on the model’s internal knowledge.

==== - The model doesn’t need a rich vocabulary if your context is lists of words or structured tokens. ====
* All reasoning is performed on the tokens you feed, so even a “small vocab” model can reason deeply if: - Hidden layers, attention, and model size are large - Context window can accommodate all tokens

: 

==== - Long-context, reasoning-optimized LLMs are what you want: - MPT-30B-Long → 65k+ tokens context, good reasoning - LLaMA 3 70B context-extended versions → huge context, strong chain-of-thought - Falcon-180B-Extended → extremely large, great for reasoning over tens of thousands of tokens - Custom LoRA or fine-tuned models built for reasoning + long context ====

: 

==== 1. Feed your structured context (lists of words, tables, or bullet points) into the model, respecting its context window. ====
# Use chain-of-thought prompts to guide reasoning step-by-step.
# Summarize / compress older context if needed to stay within the window.
# Because you control all the input, vocabulary richness doesn’t matter — reasoning capacity and context handling are the bottlenecks.

===== : =====
* The model acts like a reasoning engine, and all “knowledge” comes from your external context.

If you want, I can make a diagram showing the reasoning pipeline:
* Large-context input (lists/words) → attention layers → hidden layers → reasoning output, showing how vocabulary is basically irrelevant.

Do you want me to do that?