LLMs improve reasoning in math, science, and coding by generating and self-filtering their own training data through cycle-consistency, factuality, and correctness checks on unlabeled prompts.
B.2 Factual Error Check The factual error check screens whether the candidate solution contains factual, arithmetic, or logical errors that would make it unsuitable for training
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Self-Verified Distillation: Your Language Model Is Secretly Its Own Synthetic Data Pipeline
LLMs improve reasoning in math, science, and coding by generating and self-filtering their own training data through cycle-consistency, factuality, and correctness checks on unlabeled prompts.