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README.md

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+---
+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
+---
+
+# LittleCurriculum
+
+> ## 🚧 Upload in progress
+> This corpus is **still uploading** — not all shards are present yet, so the dataset is
+> **incomplete**. Please **do not download or benchmark** until this notice is removed.
+> Expected complete: within a few hours. Thanks for your patience.
+
+**LittleCurriculum** is an ≈88-billion-token English pretraining corpus distilled from
+[FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) through a five-stage
+filtering pipeline aligned with U.S. Common Core standards for grades **K–5**. Content, facts, and
+vocabulary characteristic of material taught **above Grade 5** are explicitly removed.
+
+It is the corpus behind the **LittleLearner** models — language models trained from scratch under a
+*pedagogically controlled knowledge boundary*, released alongside matched unfiltered controls. See the
+[LittleLearner model collection](https://huggingface.co/littlelearner).
+
+> **A note on scope.** LittleCurriculum constrains the *academic curriculum content* of the training
+> distribution — not the total world knowledge of a child. Everyday vocabulary that a 10-year-old
+> encounters outside the classroom (e.g. "adrenaline rush", "hormone-free" on a food label) can still
+> appear, in grade-appropriate registers. The filter targets advanced *concepts and skills*, not the
+> mere presence of a word. See **Limitations** below.
+
+## Dataset at a glance
+
+| | |
+|---|---|
+| Tokens | ≈88B (measured ≈86–88B; tokenizer-dependent) |
+| Documents | ~242M |
+| Shards | 2,546 Parquet files (`shard_00000.parquet` … `shard_02545.parquet`) |
+| Compression | ZSTD |
+| Schema | `id` (string), `text` (string) |
+| Language | English |
+| Source | FineWeb-Edu (CommonCrawl-derived) |
+| License | ODC-By 1.0 |
+
+*Token counts were measured with the LittleLearner tokenizer (vocab 32,768; ≈4.43 chars/token). The
+paper reports the corpus as "88B tokens"; the LittleLearner models were each trained on **80B tokens**
+(≈0.9 epoch of this corpus) — the corpus size and the training budget are distinct quantities.*
+
+## Provenance
+
+```
+CommonCrawl  →  FineWeb-Edu  →  [5-stage K–5 filter]  →  LittleCurriculum
+```
+
+## Filtering pipeline
+
+The corpus is produced by applying five sequential filters to FineWeb-Edu documents. The reusable,
+self-contained implementation is released as supplementary material with the paper.
+
+1. **Age-of-Acquisition + Zipf.** Drop a document if its out-of-vocabulary fraction exceeds 5%, or if
+   the 95th-percentile word Age-of-Acquisition exceeds 12 years. Unknown words are imputed an AoA from
+   Zipf word frequency.
+2. **FastText grade classifier.** Keep a document only if a 4-class (K5 / K8 / K12 / out-of-scope)
+   fastText classifier assigns it the **K5** label.
+3. **ModernBERT grade classifier.** Keep a document only if a fine-tuned ModernBERT sequence classifier
+   assigns it the **K5** class (independent second classifier; documents must pass *both*).
+4. **Symbolic filter.** Drop documents matching any of ~28 advanced-mathematics / notation patterns
+   (exponents, radicals, integrals/sums, `\frac`, function notation, chemical formulas, etc.).
+5. **Word-list filter.** Drop documents containing any word from a corpus-derived blocklist of terms
+   strongly associated with beyond-K–5 material (log-odds `delta_k5 ≤ -4`).
+
+## Intended use
+
+Research on **controlled knowledge exposure**: studying whether a capability was *learned* from the
+training distribution or merely *elicited* by post-training/scaling/prompting, when the training
+distribution's coverage is explicitly specified. Suitable for pretraining language models with an
+interpretable knowledge boundary, continual-learning and boundary-probing studies, and machine-vs-child
+learning comparisons.
+
+## Limitations
+
+- **Not zero-exposure.** The filter removes advanced academic *content and skills*, not every token. A
+  measured spot-check found beyond-curriculum vocabulary present only at low, grade-appropriate
+  frequency (e.g. "adrenaline" 0.075%, "hormone" 0.177%, "epinephrine" 0.006%, "ballistic missile"
+  0.001% of documents), overwhelmingly in everyday registers (news/history, food labels, first-aid,
+  narrative) rather than advanced technical instruction. The capability-boundary claim concerns
+  *skills*, not *lexical exposure*.
+- **Evaluation scope.** In the accompanying paper, the "elicitation, not acquisition" finding is
+  evaluated primarily on **mathematical reasoning** (MathCAMPS, accuracy by grade). General-knowledge
+  boundary behavior is characterized qualitatively via targeted probes rather than a systematic
+  benchmark.
+- **Inherited biases.** As a filtered subset of FineWeb-Edu (web text), the corpus inherits the
+  coverage, quality, and biases of that source and of CommonCrawl.
+- **Classifier imperfection.** The grade classifiers and heuristics are imperfect; both false drops
+  (in-scope text removed) and false keeps (borderline text retained) occur.
+
+## License & attribution
+
+Released under the **Open Data Commons Attribution License (ODC-By) v1.0**, inherited from FineWeb-Edu.
+Use requires attribution to this dataset and to the upstream source. Underlying text originates from
+CommonCrawl; please also observe the CommonCrawl terms of use.
+
+## Citation
+
+```bibtex
+@article{littlelearner2026,
+  title   = {LittleLearner: Language Models Under Pedagogically-Controlled Knowledge Exposure},
+  author  = {Fanfei Li and Jana Zeller and Manuel Prada-Corral and Thadd{\"a}us Wiedemer
+             and Prasanna Mayilvahanan and Ryan Cotterell and Wieland Brendel},
+  journal = {arXiv preprint arXiv:2608.13545},
+  year    = {2026},
+  url     = {https://arxiv.org/abs/2608.13545}
+}
+```
+
+Please also cite **FineWeb-Edu** (Penedo et al., 2024) as the upstream source.