COIG-Writer: A High-Quality Dataset for Chinese Creative Writing with
  Thought Processes
Authors: name: Yunwen Li, name: Shuangshuang Ying, name: Xingwei Qu, name: Xin Li, name: Sheng Jin, name: Minghao Liu, name: Zhoufutu Wen, name: Tianyu Zheng, name: Xeron Du, name: Qiguang Chen, name: Jiajun Shi, name: Wangchunshu Zhou, name: Jiazhan Feng, name: Wanjun Zhong, name: Libo Qin, name: Stephen Huang, name: Wanxiang Che, name: Chenghua Lin, name: Eli Zhang
Organization: Multimodal Art Projection

Large language models exhibit systematic deficiencies in creative writing,
particularly in non-English contexts where training data is scarce and lacks
process-level supervision. We present COIG-Writer, a novel Chinese creative
writing dataset that captures both diverse outputs and their underlying thought
processes through systematic reverse-engineering of high-quality texts. Unlike
existing datasets that provide only input-output pairs, COIG-Writer comprises
1,665 meticulously curated triplets spanning 51 genres, each containing: (1) a
reverse-engineered prompt, (2) detailed creative reasoning documenting
decision-making processes, and (3) the final text. Through comprehensive
experiments, we identify a two-component model of creative writing: narrative
logic (provided by process supervision) and linguistic expression (maintained
by general-purpose data). Our findings reveal three critical insights: (1)
Process supervision is highly effective but requires stabilization with general
data. A ratio of at least one creative sample to twelve general samples is
needed to achieve optimal performance; below this threshold, the win rate
progressively degrades (from 62.75% down to 35.78%)., (2) creative capabilities
are culturally-bound with no cross-lingual transfer (89.26pp gap between
Chinese and English performance), and (3) lexical diversity inversely
correlates with creative quality (TTR paradox), suggesting high diversity
signals compensatory behavior for logical deficiencies. These findings
establish that creative excellence emerges from the interaction between logical
scaffolding and linguistic grounding, analogous to how mathematical reasoning
enhances but cannot replace linguistic competence in foundation models.