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Self-consistency (SC) has been a widely used decoding strategy forchain-of-thought reasoning. Despite bringing significant performanceimprovements across a variety of multi-step reasoning tasks, it is a high-costmethod that requires multiple sampling with the preset size. In this paper, wepropose a simple and scalable sampling process, \textbf{E}arly-Stopping\textbf{S}elf-\textbf{C}onsistency (ESC), to greatly reduce the cost of SCwithout sacrificing performance. On this basis, one control scheme for ESC isfurther derivated to dynamically choose the performance-cost balance fordifferent tasks and models. To demonstrate ESC's effectiveness, we conductedextensive experiments on three popular categories of reasoning tasks:arithmetic, commonsense and symbolic reasoning over language models withvarying scales. The empirical results show that ESC reduces the average numberof sampling of chain-of-thought reasoning by a significant margin on sixbenchmarks, including MATH (-33.8%), GSM8K (-80.1%), StrategyQA (-76.8%),CommonsenseQA (-78.5%), Coin Flip (-84.2%) and Last Letters (-67.4%), whileattaining comparable performances.

Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step Reasoning