LittleLearner: Language Models Under Pedagogically Controlled Knowledge Exposure
Modern language models are trained on heterogeneous web-scale text corpora. Consequently, studying knowledge and skill acquisition is difficult, as prior exposure to related content is hard to characterize. To address this challenge, we introduce LittleCurriculum, a curated 88B-token pretraining corpus tailored to U.S. elementary school material, explicitly excluding concepts, facts, and vocabulary taught above Grade 5. Training a 5B-parameter LLM from scratch on LittleCurriculum yields LittleLearner, a model with sufficient language competence for open-ended evaluation, yet with clear knowledge and capability boundaries mapped to interpretable curriculum guidelines. We release LittleCurr...
| Authors | Fanfei Li, Jana Zeller, Manuel Prada-Corral et al. |
|---|---|
| Primary category | cs.CL |
| Published | 13 August 2026 |
| Last updated at source | 7 October 2026 |
Connected in the knowledge graph
- littlelearner/LittleCurriculum-ChatDataset · cited by
- littlelearner/LittleCurriculumDataset · cited by
- littlelearner/littlelearner-5b-chattyModel · cited by
- littlelearner/littlelearner-0.6b-chattyModel · cited by
- littlelearner/littlelearner-5b-baseModel · cited by
- littlelearner/littlelearner-0.6b-baseModel · cited by
Models and datasets referencing this paper
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