Arithmetic-XL

12 Million Verified Arithmetic Examples for Language Model Pretraining

"Arithmetic is one of those things that humans learn in primary school but language models somehow manage to forget halfway through training."

Arithmetic-XL is a large-scale arithmetic pretraining dataset containing over 12 million procedurally generated and verified examples designed to improve numerical reasoning in language models.

The dataset was created with one objective:

Teach models how to think about arithmetic instead of simply memorizing answers.

Unlike many arithmetic datasets that focus only on direct question-answer pairs, Arithmetic-XL follows a layered curriculum that gradually introduces numerical concepts, multi-step computation, operator precedence, compositional reasoning, adversarial examples, and arithmetic embedded inside natural language.

Whether you're training a 5M parameter TinyLM or a multi-billion parameter foundation model, Arithmetic-XL provides a diverse collection of arithmetic examples suitable for pretraining, continued pretraining, and specialized arithmetic fine-tuning.

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Why Another Arithmetic Dataset?

While training language models, I kept running into the same frustrating problem.

The model could explain surprisingly complicated topics...

...and then confidently tell me that:

17 × 8 = 121

That felt slightly concerning.

I searched for arithmetic datasets that were:

• Large
• Diverse
• Procedurally verified
• Suitable for pretraining
• Rich enough to teach actual arithmetic patterns rather than simple memorization

I found some excellent datasets.

I also found datasets that contained the same question written five hundred different ways.

After enough searching, I reached the conclusion every developer eventually reaches:

"Fine... I'll build it myself."

Several weeks, countless Python scripts, and far too many generated equations later...

Arithmetic-XL was born.

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What Makes Arithmetic-XL Different?

Arithmetic-XL is not simply a collection of equations.

It follows a structured curriculum inspired by how humans progressively learn arithmetic.

Instead of immediately exposing the model to complicated expressions, the dataset gradually increases difficulty through multiple specialized layers.

The goal is to help models build numerical intuition before introducing increasingly complex reasoning tasks.

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Dataset Overview

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Property Value

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Dataset Name Arithmetic-XL

Total Examples 12,000,000+

Generation Method Hybrid Procedural + LLM Assisted

Verification Computationally Verified

Primary Language English

Domain Arithmetic Reasoning

Recommended Usage Pretraining / Continued Pretraining / Fine-tuning

License MIT