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This paper proposes STOP (SuperTOken for Pruning), a systematic framework for pruning inefficient reasoning paths early in parallel reasoning with Large Reasoning Models. The method achieves superior efficiency and effectiveness across models from 1.5B to 20B parameters, boosting GPT-OSS-20B accuracy on AIME25 from 84% to 90% under fixed compute budgets.