Why Sampling More Can Make LLM Reasoners Worse: Modal and Correlation Ceilings
Scaling test‑time sampling in language‑model reasoning looks like a win until two hard limits appear. The modal ceiling caps how many draws are needed before the most common answer is fixed—often wrong—while the correlation ceiling shows extra samples become dependent and degrade performance. Beyond these points extra computation just overthinks and harms accuracy.