The Architecture of Reason
An exploration of reasoning capabilities in modern language models and architectural patterns that enable multi-step logical inference.
Modern language models exhibit emergent reasoning abilities, chain-of-thought, mathematical problem-solving, multi-step planning, that were not explicitly trained. But which architectural choices actually enable these capabilities, and how can we design models that reason more reliably?
We analyzed 50+ published models across scales (7B to 70B parameters) and training regimes, correlating architectural features, depth, width, attention patterns, mixture-of-experts routing, with performance on standardized reasoning benchmarks.
Our key hypothesis: reasoning emerges when the model has sufficient representational capacity to maintain intermediate state across many reasoning steps, and when the training distribution includes enough structured reasoning examples. Depth matters more than width for long-chain reasoning.
We experimented with hybrid architectures that separate "working memory" layers from "reasoning" layers, finding modest gains on mathematical and logical benchmarks. The full paper includes ablation studies and recommendations for practitioners.
Key Findings
- 1Depth-to-width ratio correlates with multi-step reasoning performance more strongly than raw parameter count.
- 2Chain-of-thought prompting effectiveness depends on the model's ability to maintain coherent state across 10+ reasoning steps.
- 3Curriculum-based training on progressively harder reasoning tasks yields better generalization than uniform difficulty.
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