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CoT-Self-Instruct: Building high-quality synthetic prompts for reasoning and non-reasoning tasks
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CoT-Self-Instruct: Building high-quality synthetic prompts for reasoning and non-reasoning tasks
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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.
Forward citations
Cited by 7 Pith papers
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Skill Self-Play: Pushing the Frontier of LLM Capability with Co-Evolving Skills
Skill Self-Play uses an evolving skill library to guide LLM self-training, improving tool-call and reasoning accuracy beyond unguided self-play on five model backbones.
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Autodata: An agentic data scientist to create high quality synthetic data
Autodata trains meta-optimized AI agents to generate superior synthetic datasets, yielding performance gains over classical methods on CS research, legal, and math reasoning tasks.
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Autodata: An agentic data scientist to create high quality synthetic data
Autodata introduces an agentic method with meta-optimization to create higher-quality synthetic data, yielding performance gains over standard methods on CS, legal, and math tasks.
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Autodata: An agentic data scientist to create high quality synthetic data
An agentic weak–strong Self-Instruct loop, optionally meta-optimized, produces synthetic data that trains small models better than standard CoT Self-Instruct across three domains.
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Less is Enough: Synthesizing Diverse Data in LLM Feature Space with Sparse Autoencoders
Coverage of sparse-autoencoder-identified task features predicts post-training performance and can guide synthesis of small, high-impact datasets (2,000 vs. 300,000 samples).
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On the Generalization Gap in Self-Evolving Language Model Reasoning
Closed-loop self-evolution on LLMs improves reasoning on Knights and Knaves tasks but plateaus short of oracle-supervised levels, with multi-turn revision nearly matching it for large models.
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Learning to Pose Problems: Reasoning-Driven and Solver-Adaptive Data Synthesis
A reasoning-driven problem generator plans synthesis directions with CoT and uses solver performance feedback to adapt difficulty, producing complementary problems that yield a 3.4% average improvement across 10 reaso...
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