When Chain-of-Thought Helps and When It Hurts: An Empirical Investigation of the Serial-Depth Bottleneck in LLM Reasoning
Source
Tughanbulut Kurtulush
Author
Tughanbulut Kurtulush
Published
Terms in this piece · Glossary
chain-of-thought — Having a model write out intermediate reasoning steps before its answer, which markedly improves performance on hard problems.
transformer — The neural network architecture behind modern AI models, built on attention — letting every word directly consider every other word in parallel.
benchmark — A standard public test set for comparing AI models — the shared scoreboards behind every "model X beats model Y" claim.
token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
Why it matters
Gives a concrete rule for when reasoning tokenThe chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.Full definition → buy accuracy and when they are wasted spend, including a case where chain-of-thoughtHaving a model write out intermediate reasoning steps before its answer, which markedly improves performance on hard problems.Full definition → actively hurt a smaller model.
