Large Reasoning Models (LRMs) achieve strong performance by generating long reasoning traces with explicit reflection. However, reflection is not functionally uniform: some reflections revise the reasoning strategy, while others verify previously derived intermediate results. In this work, we examine whether local self-verification can be controlled selectively without suppressing useful reasoning. Through a large-scale analysis of mathematical reasoning traces, we distinguish strategy-level rethink from local self-verification recheck, and further separate rechecks into corrective and confirmatory cases. We find that recheck occurs frequently across models and benchmarks, but most observed rechecks are confirmatory, rarely uncovering errors or changing the reasoning trajectory. Motivated by this finding, we propose Experience-Driven Suppression (EDS), a conservative test-time controller that suppresses a recheck only when historical outcomes of similar episodes suggest low utility. Unlike early-exit or trigger-suppression methods that stop reasoning globally or suppress reflection cues broadly, EDS targets local recheck while preserving later rethink. Experiments show that EDS yields moderate reasoning-length reductions with limited accuracy changes, supporting behavior-specific rather than monolithic reflection control.