Too Smart to Teach: The Articulability Ceiling in Language Models

Larger language models solve harder problems, but do they also produce proportionally better explanations? We study in-context explanation transfer across 40 open-weight models (360M–72B) and 5 receivers (360M–7B), using problems drawn from 13 benchmark sources and evaluated with domain-specific metrics. Our central finding is an articulability ceiling: under equal token budgets, mid-range models are better teachers than frontier models. Transfer peaks at 170–220 words and then drops sharply beyond 300 words, falling below even the shortest explanations. Large models partly compensate through verbosity: longer explanations yield higher total transfer on average, but each additional word carries less pedagogical value, so a 360M model delivers more transfer value per word than a 72B model. We also find that RLHF improves pedagogical clarity: instruct models score better on LLM-judge evaluation than base models despite lower lexical overlap, and teacher correctness dominates all other factors. Code and logs are released with the paper.