Large reasoning language models (LRLMs) achieve strong performance on complex reasoning tasks by generating long chain-of-thought trajectories, but such long-form reasoning also increases token consumption and latency. Existing inference-time methods that do not fine-tune the target LRLM optimize long reasoning through budgeting, early exit, uncertainty control, or reflection suppression, but they often treat reflection as a local signal rather than as a factor shaping the reasoning trajectory. In this paper, we identify unregulated reflection as a source of inefficient reasoning and characterize two failure modes: Trajectory Drift, where intermediate reflection opens weakly related branches that restart, reorient, or detour from the current path, and Verification Saturation, where answer-adjacent reflection repeatedly checks or restates an already-emerged provisional answer. To address these issues, we propose Regulated Reflection Decoding (RRD), an inference-time framework that requires no fine-tuning or parameter updates to the target LRLM and treats reflection events as online control points. RRD combines Trajectory Drift Suppression (TDS), which guides intermediate reasoning toward validated continuation branches, and Redundant Verification Suppression (RVS), which suppresses redundant verification when the provisional answer is sufficiently reliable. Experiments on multiple reasoning benchmarks show that RRD reduces reasoning length while preserving or improving accuracy, yielding a stronger accuracy-efficiency trade-off than direct inference and representative inference-time baselines.