---
license: odc-by
language:
- en
pretty_name: LittleCurriculum
size_categories:
- 100M<n<1B
task_categories:
- text-generation
tags:
- pretraining
- curriculum-learning
- education
- fineweb-edu
- knowledge-boundary
- common-core-standards

---

# LittleCurriculum

**LittleCurriculum** is an ~88B-token English pretraining corpus derived from [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu-score-2). It is filtered to align with U.S. Common Core standards for grades **K–5**, removing documents containing academic concepts and skills characteristic of later grades.

It is the training corpus for the **LittleLearner** models, designed to study language models under a controlled knowledge boundary. See the [LittleLearner paper](https://arxiv.org/abs/2608.13545) for methodology, validation, and experiments.

## Dataset

* **Documents:** ~244M
* **Tokens:** ~88B
* **Language:** English
* **Source:** FineWeb-Edu
* **Format:** Parquet (`id`, `text`)
* **License:** ODC-By 1.0

## Dataset Construction 

LittleCurriculum is produced from FineWeb-Edu using five sequential filters:

1. Age-of-Acquisition and word-frequency filtering
2. fastText grade-level classification
3. ModernBERT grade-level classification
4. Advanced mathematical/symbolic notation filtering
5. Beyond-K–5 vocabulary filtering

The filters constrain the developmental level of a text, i.e. the knowledge, complexity, and reasoning it demands, not its subject matter. Advanced **Beyond-K–5 concepts** are removed, but topic is deliberately not curated: everyday material on any subject a curious learner might explore—aviation, current events, crime in the news—can appear when written at a K–5 level. Likewise, words associated with advanced topics may occur in ordinary, grade-appropriate contexts. Full construction details, thresholds, validation, and limitations are described in the [paper](https://arxiv.org/abs/2608.13545). 

## Citation
```bibtex
@misc{li2026littlelearnerlanguagemodelspedagogically,
      title={LittleLearner: Language Models Under Pedagogically Controlled Knowledge Exposure}, 
      author={Fanfei Li and Jana Zeller and Manuel Prada-Corral and Thaddäus Wiedemer and Prasanna Mayilvahanan and Ryan Cotterell and Wieland Brendel},
      year={2026},
      eprint={2608.13545},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2608.13545}, 
}
```