Sibling-guided critique and revision of MCTS reasoning traces yields a 30K-sample dataset that matches or beats 590K-sample baselines on the MATH benchmark for 7B models.
Self-RAG: Learning to re- trieve, generate, and critique through self-reflection
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SIGMA: Refining Large Language Model Reasoning via Sibling-Guided Monte Carlo Augmentation
Sibling-guided critique and revision of MCTS reasoning traces yields a 30K-sample dataset that matches or beats 590K-sample baselines on the MATH benchmark for 7B models.