Self-Consistency Boosts Language Model Reasoning
ai•Jul 8, 2026

Self-Consistency Boosts Language Model Reasoning

The authors propose Self-Consistency, a simple decoding strategy that produces remarkable improvements across arithmetic, common sense, and symbolic reasoning tasks by comparing multiple lines of thought.

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Key summary

This paper introduces Self-Consistency, a decoding strategy that generates multiple independent reasoning paths and selects the answer that appears most consistently among them, improving language model reasoning.

TL;DR

  • Self-Consistency generates multiple reasoning paths to improve language model reasoning.
  • The approach is simple and doesn't require additional training or supervision.
  • It produces remarkable improvements across various reasoning tasks.
  • Self-Consistency is a natural successor to Chain-of-Thought prompting.
  • It shows that substantial gains can come from making better use of existing reasoning abilities.
  • The approach has long-term impact on language model research and influences later work.
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