Sampling multiple solutions and voting may be a strong label-free path to better reasoning
iatitov · x · 2026-07-21
The post argues that sampling many solutions and taking the majority answer is a highly reliable, label-free way to improve LLM reasoning.
The key idea is that many existing training methods compress consensus into a filter, a preference signal, or a scalar reward. The post suggests an alternative: preserve that consensus structure and let the model read it directly. The shared diagram frames the pipeline as sample rollouts, consensus, teacher construction, and distillation.
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