core concepts

Reasoning Model

AI models specifically designed or trained to perform step-by-step logical reasoning before answering. Reasoning models use techniques like chain-of-thought and extended thinking to solve complex problems more reliably.

In practice

OpenAI's o1 and o3 models use extended reasoning to spend more time thinking before answering complex math and science problems.

Chain of Thought
A prompting technique that encourages language models to show their reasoning step by step before arriving at a final answer. Chain-of-thought prompting significantly improves performance on mathematical, logical, and multi-step reasoning tasks.
Large Language Model
A neural network trained on massive text datasets to understand and generate human language. LLMs use billions of parameters to capture statistical patterns in language, enabling them to perform tasks like translation, summarization, and code generation.
Emergent Abilities
Capabilities that appear in larger models but are absent in smaller ones, seemingly arising spontaneously at certain scale thresholds. Emergent abilities include tasks like multi-step reasoning, arithmetic, and code generation that smaller models cannot perform.