StepBack: Step-Level Error-Localized Resampling for Efficient Test-Time Reasoning

Self-consistency (SC) is a widely used decoding strategy for improving chain-of-thought reasoning, but it requires sampling many complete reasoning trajectories with a preset budget. Its efficient variants reduce the cost mainly by stopping full-trajectory sampling early or distributing the number of samples across questions. In this paper, we propose \textbf{StepBack}, a step-level error-localized resampling method for efficient test-time reasoning. Given a small set of draft trajectories, \method localizes potential first error positions using lightweight signals and then resamples the detected error step and its suffix, followed by majority answer voting. Our theoretical analysis explains why resampling before the localized error point can increase the probability of recovering a correct final answer. Empirically, \method consistently matches or exceeds SC across mathematical reasoning and multi-hop QA benchmarks, while reducing token usage by up to 50\%. In addition, by nature, \method can be combined with adaptive inference methods such as Adaptive Consistency and Early-Stopping SC, further improving their efficiency and scalability.