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README.md

@@ -13,47 +13,124 @@ tags:
 - education
 - fineweb-edu
 - knowledge-boundary
-- common-core-standards
-
+- common-core
 ---
 
 # LittleCurriculum
-**🚧 Upload in progress, do not benchmark yet!**
 
-**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.
+**LittleCurriculum** is a **developmentally restricted** ≈88-billion-token English pretraining corpus:
+a **precision-first** subset of [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu)
+filtered to the U.S. **K–5** developmental level. A multi-stage pipeline enforces constraints on
+**knowledge, syntactic complexity, and reasoning** (with Common Core State Standards as the grade-level
+reference), removing material characteristic of the **Beyond-K–5** curriculum—advanced concepts such as
+algebra or higher-grade science—while retaining text a K–5 learner can process.
+
+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.** The pipeline constrains the **developmental level** of a text—the knowledge,
+> linguistic complexity, and reasoning it demands—not its subject matter. Advanced **Beyond-K–5 concepts**
+> (calculus, quantum physics, and the like) are filtered to near-zero, but subject matter itself is not
+> curated: everyday content on any topic a curious child might follow—aviation, current events, crime in
+> the news—can appear when written at a K–5 level. The design is deliberately *precision-first*, trading
+> recall for a sharp, interpretable boundary (validated on CommonCoreText and WeeBit; see paper §3.1).
+> See **Scope of filtering** and **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.
 
-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.
+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`).
 
-## Dataset
+## Intended use
 
-* **Documents:** ~242M
-* **Tokens:** ~88B
-* **Language:** English
-* **Source:** FineWeb-Edu
-* **Format:** Parquet (`id`, `text`)
-* **License:** ODC-By 1.0
+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.
 
-## Dataset Construction 
+## Scope of filtering: developmental level, not topic
 
-LittleCurriculum is produced from FineWeb-Edu using five sequential filters:
+Consistent with the paper, the pipeline enforces constraints on **knowledge, syntactic complexity, and
+reasoning**—the *developmental level* a text demands—rather than curating subject matter. Two things
+follow: (i) advanced **Beyond-K–5 concepts** (e.g. calculus, quantum physics) are removed to near-zero;
+and (ii) subject matter is otherwise not restricted, so everyday content on any topic a curious child
+might follow—aviation, current events, crime in the news—can appear when written at a K–5 level. The
+filter is *precision-first*: tuned for near-zero Beyond-K–5 retention at the cost of discarding much
+in-scope material, yielding a sharp, interpretable boundary rather than an exhaustive sample of K–5 text.
 
-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
+## Limitations
 
-The dataset constrains **academic curriculum content**, not all lexical or real-world exposure. Words associated with advanced topics may still occur in everyday, grade-appropriate contexts. Full construction details, thresholds, validation, and limitations are described in the [paper](https://arxiv.org/abs/2608.13545). 
+- **Developmental-level filter, not topical curation.** See **Scope of filtering** above — the pipeline
+  removes Beyond-K–5 *concepts*, not general subject matter, which can span any domain at a K–5 level.
+- **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
-@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}, 
+@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.