Diverse Conversation Category Stratification

via “category-stratified dialogue sampling for balanced training”

200K high-quality multi-turn dialogues for instruction tuning.

Unique: Explicitly structures dataset into three semantic categories (world knowledge, creative, task assistance) with maintained stratification during curation, rather than treating all conversations as undifferentiated — this enables category-aware training strategies and prevents single-domain overfitting

vs others: More structured than generic conversation datasets (e.g., raw Reddit or web scrapes) because category labels enable curriculum learning; more flexible than single-domain datasets because it covers multiple dialogue types in one corpus