  Human priors play a crucial role in efficiently utilizing data in deep
learning. However, with the development of large language models (LLMs), there
is an increasing emphasis on scaling both model size and data volume, which
often diminishes the importance of human priors in data construction.
Influenced by these trends, existing Small Language Models (SLMs) mainly rely
on web-scraped large-scale training data, neglecting the proper incorporation
of human priors. This oversight limits the training efficiency of language
models in resource-constrained settings. In this paper, we propose a principle
to leverage human priors for data construction. This principle emphasizes
achieving high-performance SLMs by training on a concise dataset that
accommodates both semantic diversity and data quality consistency, while
avoiding benchmark data leakage. Following this principle, we train an SLM
named HARE-1.1B. Extensive experiments on large-scale benchmark datasets
demonstrate that HARE-1.1B performs favorably against state-of-the-art SLMs,
validating the effectiveness of the proposed principle. Additionally, this
provides new insights into efficient language model training in
resource-constrained environments from the view of human priors.
