Synthetic Algebra Math Reasoning Dataset with 125K Problems

Sys2Rreasoning 125000 is a synthetic dataset containing algebra-heavy math reasoning problems, created by author xortron. Each entry includes fields for the question, problem, solution method, and answer. The dataset is tagged for fine-tuning and contains text-based word problems in English.

Use Cases

• Train a sequence-to-sequence model to generate the answer field from the question and problem fields for automated math problem solving.
• Fine-tune a language model on the how_to_solve field to generate step-by-step reasoning for algebra word problems.
• Analyze the relationship between problem complexity and the structure of the how_to_solve explanations across 125,000 entries.
• Build a classifier using the problem text to categorize question types or difficulty levels for educational applications.

Strengths

• Contains 125,000 entries of synthetic math reasoning data, providing substantial volume for model training.
• Includes multiple structured text fields (question, problem, how_to_solve, answer) supporting diverse training objectives.
• Explicitly tagged for fine-tuning and math reasoning applications, indicating a clear intended use.

Limitations

• The synthetic nature of the data may not fully capture the complexity or distribution of real-world math problems.
• Lack of metadata on problem difficulty or source limits stratified analysis and curriculum alignment.
• Unknown column definitions and sample data hinder precise assessment of data structure and quality.

Provenance

Source

huggingface, author xortron

Collection Method

Synthetically generated

Freshness

Last updated on 2026-01-02, indicating recent maintenance.

Geography

Region tagged as 'us', but specific spatial coverage is unknown.