Why 'think step by step' boosts LLM reasoning

Chain-of-thought prompting works because it gives a transformer more forward passes per problem, not because it makes the model smarter. Each reasoning token triggers a new pass through the fixed-depth network, turning the model into a loop. The written steps act as external memory, letting the model read back intermediate values instead of holding them in internal state.