How does inference-time ensemble voting improve ChatGPT reliability and reasoning robustness?

Inference-time ensemble voting aggregates multiple model outputs to improve reliability and reduce hallucinations.

Inference-time ensembling runs multiple independent or semi-independent generations (from the same or different model checkpoints) and aggregates results via voting, ranking, or scoring.

This reduces variance in outputs and improves robustness for reasoning tasks. It is especially useful in scenarios where single-sample decoding may produce inconsistent or hallucinated answers.

Aggregation methods include majority voting, reward model scoring, or consistency-based ranking.