Add dataset card

README.md

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+---
+license: odc-by
+task_categories:
+  - text-generation
+  - question-answering
+language:
+  - en
+tags:
+  - synthetic
+  - sft
+  - instruction-tuning
+  - education
+size_categories:
+  - 100K<n<1M
+configs:
+  - config_name: math
+    data_files: math.parquet
+  - config_name: general_knowledge
+    data_files: general_knowledge.parquet
+---
+
+# LittleCurriculum-Chat
+
+Synthetic K–5 chat data generated with Gemini 2.5 Flash and filtered with the
+[LittleCurriculum filter](https://github.com/littlelearner-ll/littlecurriculum-filter).
+Used to train the [LittleLearner](https://huggingface.co/littlelearner) models.
+
+| Config | Rows | Seeded from | Content |
+|---|---|---|---|
+| `math` | 79,543 | MegaMath-Web-Pro-Max | Word problems with step-by-step worked solutions |
+| `general_knowledge` | 484,787 | [LittleCurriculum](https://huggingface.co/datasets/littlelearner/LittleCurriculum) | Reading comprehension, factual QA, explanation, summarisation, definitions |
+
+Seeds are real documents rather than topic prompts, which keeps the topic
+distribution broad. Every row carries a `messages` column in
+`[{role, content}]` form alongside the structured fields.
+
+```python
+from datasets import load_dataset
+ds = load_dataset("littlelearner/LittleCurriculum-Chat", "general_knowledge")
+```
+
+## Related data
+
+For instruction fine-tuning we additionally used SmolTalk, MMLU
+auxiliary-train and ARC, each filtered with our pipeline. For GRPO we used
+grade-stratified synthetic problems from the MathCAMPS pipeline alongside
+GSM8K, again filtered with our pipeline. We do not redistribute these — they
+derive from public datasets and reproduce in one command:
+
+```bash
+python filter_k5.py --hf-dataset HuggingFaceTB/smol-smoltalk --out smoltalk_k5.parquet
+```
+
+In our runs this data was most useful mixed into pretraining and midtraining
+rather than reserved for a dedicated SFT stage.
+
+## License
+
+ODC-By 1.0, inherited from FineWeb-Edu via LittleCurriculum. Generated with
+Google Gemini.
+
+## Citation
+
+```bibtex
+@misc{li2026littlelearner,
+      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},
+}
+```