Applying Small Language Models (SLMs) to Chinese character-driven generation remains challenging due to data scarcity and the difficulty of disentangling character style. Standard Supervised Fine-Tuning (SFT) often captures surface-level semantics but produces frequent Out-Of-Character (OOC) outputs. We frame this as a controlled sentence-level style rewriting task, which isolates stylistic quality from dialogue context management. We propose a Structured Style-Rewrite Framework that decomposes character style into interpretable format signature, syntactic, and pragmatic dimensions, combined with Chain-of-Thought (CoT) supervision for explicit style planning. A CoT-Shared Direct Preference Optimization (DPO) stage further aligns style planning with surface realization by ensuring preference learning targets output-level style execution rather than reasoning trace differences. Experiments across eight characters from four diverse source domains demonstrate that our method enables a Qwen3-1.7B model to achieve a Valid Style Score of $0.632$ while maintaining strong semantic fidelity (0.878), placing on the Pareto frontier among the evaluated systems and outperforming significantly larger baselines (e.g., GLM-4.7) on consumer hardware.

翻译：将小型语言模型应用于中文角色驱动生成仍面临挑战，主要源于数据稀缺及角色风格解耦困难。标准监督微调虽能捕捉表层语义，但常产生角色出位输出。我们将此问题构建为可控的句子级风格重写任务，从而将风格质量与对话上下文管理相分离。提出结构化风格重写框架，将角色风格分解为可解释的格式特征、句法特征与语用特征三个维度，并结合思维链监督实现显式风格规划。进一步设计共享思维链的直接偏好优化阶段，通过确保偏好学习目标聚焦于输出级风格执行（而非推理轨迹差异），实现风格规划与表层实现的协同对齐。在来自四个不同源域的八个角色上的实验表明，本方法使Qwen3-1.7B模型在保持较高语义保真度（0.878）的同时达到0.632的有效风格得分，处于评估系统的帕累托前沿，且在消费级硬件上性能显著优于更大规模基线模型（如GLM-4.7）。