Definition
Self-consistency is a prompting technique, introduced by Wang and colleagues in 2022, that improves reasoning reliability by sampling multiple independent reasoning paths and then taking a majority vote on the final answer. Rather than accepting the single answer a model produces on its first attempt, self-consistency runs the model several times, lets it reason through the problem in different ways, and selects the answer that appears most often across those runs.
The intuition
When a model reasons step by step, the first path it takes is not necessarily the best one. Complex problems often admit several valid routes to the same correct answer. If you sample a diverse set of reasoning chains and many of them converge on the same conclusion, you can be more confident that conclusion is right. Wrong answers, by contrast, tend to scatter — different mistakes lead to different incorrect results, so no single wrong answer dominates the vote.
How it is used
Self-consistency layers on top of step-by-step reasoning prompts. You enable sampling (so each run varies), generate several complete reasoning traces, extract the final answer from each, and pick the most frequent one. It has been shown to improve greedy step-by-step prompting by a large margin across arithmetic, commonsense, and symbolic reasoning tasks.
The trade-off is cost: producing N answers takes roughly N times the compute. For sovereign Bitcoiners running local models, self-consistency is a tunable knob — spend more inference to raise reliability on questions that matter. It complements exploratory methods like tree-of-thought and is a standard part of advanced prompt engineering.
Cost the multiple samples in the inference cost calculator.
In Simple Terms
Self-consistency is a prompting technique, introduced by Wang and colleagues in 2022, that improves reasoning reliability by sampling multiple independent reasoning paths and then taking…