sileod/reasoning-core

Procedural symbolic reasoning data generators suite for synthetic pretraining

42
/ 100
Emerging

This tool generates high-quality, diverse textual datasets for training and evaluating large language models (LLMs). It takes descriptions of symbolic and algorithmic tasks, like formal logic problems or arithmetic, and outputs complex question-answer pairs. It is designed for AI researchers and machine learning engineers who need large volumes of specialized synthetic data to improve LLM reasoning capabilities.

Use this if you are a researcher or engineer looking to pre-train or fine-tune large language models on complex symbolic and algorithmic reasoning tasks.

Not ideal if you are looking for a tool to process or analyze real-world, natural language data for tasks like sentiment analysis or text summarization.

AI-training-data LLM-pretraining synthetic-data-generation reasoning-evaluation formal-logic-datasets
No Package No Dependents
Maintenance 13 / 25
Adoption 7 / 25
Maturity 16 / 25
Community 6 / 25

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Stars

35

Forks

2

Language

Python

License

MIT

Last pushed

Mar 27, 2026

Commits (30d)

0

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