LittleLearner: Language Models Under Pedagogically Controlled Knowledge Exposure

• URL: http://arxiv.org/abs/2608.13545v1
• Date: Thu, 13 Aug 2026 17:56:12 GMT
• Title: LittleLearner: Language Models Under Pedagogically Controlled Knowledge Exposure
• Abstract summary: We release LITTLECURRICULUM and LITTLELEARNER as a developmentally restricted sandbox to study how models acquire, represent, and use data.<n>We illustrate the sandbox's utility in a first suite of experiments on injecting new knowledge through post-training and in-context learning.
• Score: 56.83509505408756
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 LITTLECURRICULUM and LITTLELEARNER as a developmentally restricted sandbox to study how models acquire, represent, and use data under a well-defined training scope. We illustrate the sandbox's utility in a first suite of experiments on injecting new knowledge through post-training and in-context learning. These methods let LITTLELEARNER better utilize existing knowledge, but do not raise out-of-scope capabilities. Our findings underscore the value of this controlled environment for future investigations.

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