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arXiv · 2507.23751

CoT-Self-Instruct: Building high-quality synthetic prompts for reasoning and non-reasoning tasks

Ping Yu·Jack Lanchantin·Tianlu Wang·Weizhe Yuan·Olga Golovneva·Ilia Kulikov·Sainbayar Sukhbaatar·Jason Weston·Jing Xu
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Abstract

We propose CoT-Self-Instruct, a synthetic data generation method that instructs LLMs to first reason and plan via Chain-of-Thought (CoT) based on given seed tasks, and then generate a new synthetic example of similar quality and complexity. This is followed by a filtering step to select high-quality data using automatic metrics, which are then used for LLM training. In verifiable reasoning, our synthetic data significantly outperforms existing training datasets, such as s1k and OpenMathReasoning, when evaluated on MATH500, AMC23, AIME24, and GPQA-Diamond. For non-verifiable instruction-following tasks, our method surpasses the performance of both human and standard Self-Instruct training data on the AlpacaEval 2.0 and Arena-Hard benchmarks.

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Ping Yu, Jack Lanchantin, Tianlu Wang, Weizhe Yuan, Olga Golovneva, Ilia Kulikov, Sainbayar Sukhbaatar, Jason Weston, Jing Xu. 2025-07-31. CoT-Self-Instruct: Building high-quality synthetic prompts for reasoning and non-reasoning tasks. https://arxiv.org/abs/2507.23751

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