Built to Read, Not to Recall: Language Skills over World Knowledge in Small Language Models

Frontier-scale language models are commonly assumed to need their full scale. We argue instead that LLM abilities fall into two families with very different scaling behavior: world knowledge — stored facts and entities — improves steeply with parameter count, whereas language skills — comprehending, extracting, and structuring given text — are largely attainable at modest sizes when cultivated deliberately. Retrieval-augmented generation (RAG) and agent pipelines keep knowledge external and rely chiefly on the latter; a compact purpose-trained model can therefore replace frontier-scale components at a fraction of the cost and energy. We test this hypothesis with Meno-Lite-0.1, a publicly released bilingual Russian–English 7B-class model whose name inverts Plato's anamnesis: the knowledge lives in the corpus, the skill in the model — built to read, not to recall. Across a 0.5B–72B backbone size ladder, knowledge benchmarks climb steeply (closed-book quiz F1 grows 21× from 0.5B to 72B) while skill benchmarks plateau after  7B, and the memorization ratio — answering from parameters instead of the given context — rises with scale. The skill-trained 7B model sits above the entire ladder on knowledge-graph construction (0.468 vs. 0.416 harmonic mean against a 32B model), reaches 92% of the 32B level on open-book multi-hop QA while retaining its backbone's knowledge level, is the most context-faithful model measured, and serves Russian text 6.2× faster than the 32B engine. We further document protocol sensitivities that can masquerade as scaling behavior. Weights, training data, and code are publicly available.