Self-Consistency

Self-consistency improves reasoning accuracy by sampling multiple independent answers to the same question and selecting the most frequent response. It leverages the observation that correct reasoning paths tend to converge on the same answer while errors are more varied. Self-consistency is most effective for tasks with a single correct answer such as math or factual QA, and works best when chain-of-thought reasoning is used to generate each candidate so the model can explore diverse reasoning paths.