Applicability of the synthetic-data curriculum and SnapPO RL methodology to lower-resource languages

Determine whether the training methodology used to develop the 102B-parameter bilingual Mixture-of-Experts language model—combining aggressive synthetic data generation for Korean, a bilingual low-to-high quality pre-training curriculum over 20 trillion tokens, and the SnapPO decoupled reinforcement learning framework—remains effective when applied to languages with less available training data than Korean, by conducting empirical validation.

Background

The report presents a methodology to build a bilingual LLM centered on Korean, addressing data scarcity through three pillars: (1) aggressive synthetic data generation totaling 4.5T tokens, (2) a bilingual, quality-progressive pre-training curriculum spanning 20T tokens, and (3) a decoupled reinforcement learning framework (SnapPO) for scalable multi-objective training.

While the approach demonstrates strong results for Korean, the authors explicitly note uncertainty about whether the same methodology will generalize to languages with even fewer resources than Korean, highlighting the need for empirical validation to establish transferability and effectiveness in more extreme low-resource settings.