Reasoning

Improve Reasoning Reliability with Self-Consistency

A language model can reach different answers to the same reasoning problem depending on how generation unfolds. One sampled path may make an arithmetic mistake, another may misread a condition, and a third may reach the correct result. If an application trusts only one path, its answer depends heavily on that single generation. Self-consistency uses this variability instead of trying to eliminate it. It samples several reasoning paths for the same problem, extracts their final answers, and chooses the answer supported by the largest share of the samples. The technique is an inference-time strategy: it does not require changing model weights.