arXiv cs.AI· Yang Ouyang, Jung-Eun Kim·· 12 小时前AI 评分34
StepBack：面向高效测试时推理的步骤级错误定位重采样方法
StepBack: Step-Level Error-Localized Resampling for Efficient Test-Time Reasoning
AI 导读
StepBack 是一种步骤级错误定位重采样方法，通过轻量信号定位草稿轨迹中可能的首个错误步骤，仅重采样该错误步骤及其后续内容，再进行多数答案投票。在七个数学推理与多跳问答基准上，其平均准确率与自一致性（SC）持平，同时最多减少 41.26% 的 token 消耗。该方法还可与 Adaptive Consistency 等自适应推理方法结合，进一步提升推理时效率。
正文 · 原文
Abstract: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 StepBack, a step-level error-localized resampling method for efficient test-time reasoning. Given a small set of draft trajectories, StepBack localizes likely first error positions using lightweight signals and then resamples the detected error step and its suffix, followed by majority answer voting. Empirically, StepBack matches the accuracy of SC on average across seven mathematical reasoning and multi-hop QA benchmarks, while reducing token usage by up to 41.26%. In addition, by nature, StepBack can be combined with adaptive inference methods such as Adaptive Consistency, further improving their inference-time efficiency.
| Comments: | NeurIPS 2026 |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2611.08403 [cs.AI] |
| (or arXiv:2611.08403v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.08403 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jung-Eun Kim [view email]
[v1]
Thu, 5 Nov 2026 18:12:49 UTC (141 KB)
来源：arXiv cs.AI · arxiv.org