OpenSeek Synthetic Reasoning Data

A massive synthetic reasoning dataset for LLMs, covering the fields of mathematics, code and general knowledge. Used for training and fine-tuning highly reasoning models.

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Size

Multi-domain data, several billion tokens, JSON structured text format

Licence

CC-BY-SA 4.0

Description

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OpenSeek Synthetic Reasoning Data is a set of data generated from automated pipelines aimed at extracting, reformulating, and structuring complex reasoning from raw texts. It brings together data from fields such as mathematics (Proof-Pile, FineMath), programming (OpenCoder, StarCoder), and general knowledge (FineWeb, Dolma). Each entry includes an instruction, a chain of thought, and a synthetic response, all in a format suitable for pre-training models.

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What is this dataset for?

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Pre-train or refine LLM models with explicit reasoning skills
Test the performance of models on complex chain-of-thought tasks
Building internal benchmarks for the validation of generative LLMs

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Can it be enriched or improved?

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Yes, the dataset can be supplemented with other sources of reasoning or adapted to specific languages and contexts. It is also possible to reinforce reasoning chains with additional annotations (for example: level of complexity, domain, logical coherence). Additionally, the build pipeline can be customized to create thematic variants.

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🔎 In summary

Criterion	Evaluation
🧩 Ease of use	⭐⭐⭐✩✩ (Advanced – requires understanding of JSON format and specific fields)
🧼 Need for cleaning	⭐⭐⭐⭐⭐ (Low – already well structured)
🏷️ Annotation richness	⭐⭐⭐⭐⭐ (Excellent – instructions, chain-of-thought, synthetic texts)
📜 Commercial license	✅ Yes (CC-BY-SA 4.0)
👨‍💻 Beginner friendly	⚠️ No – rather intended for experienced NLP teams
🔁 Fine-tuning ready	🎯 Perfect for training or fine-tuning complex reasoning models
🌍 Cultural diversity	⚠️ Moderate – mainly English, but adaptable