Synthetic Reasoning Examples for Instruction Tuning

OpenThoughts-1k-sample provides 1,000 high-quality synthetic examples for training reasoning models. This subset was used to fine-tune the OpenThinker-7B and OpenThinker-32B models, created by author ryanmarten and last updated in August 2025.

Use Cases

• Fine-tune language models on math problem-solving examples from the dataset.
• Train models to generate code solutions using programming examples.
• Improve model reasoning on science and puzzle-related questions.
• Benchmark model performance on diverse synthetic reasoning tasks.

Strengths

• Contains 1,000 high-quality, curated examples.
• Covers multiple reasoning domains: math, science, code, and puzzles.

Limitations

• Small sample size of 1,000 rows limits training scale.
• Synthetic nature may not fully reflect real-world reasoning patterns.
• Unknown if the data contains class imbalances across domains.

Provenance

Source

ryanmarten on Hugging Face.

Collection Method

Synthetic generation; a sample from the larger OpenThoughts-114k dataset.

Time Range

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Freshness

Last updated on 2025-08 31.

Geography

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