The screenplay serves as the foundation for television production, defining narrative structure, character development, and dialogue. While Large Language Models (LLMs) show great potential in creative writing, direct end-to-end generation approaches often fail to produce well-crafted screenplays. We argue this failure stems from forcing a single model to simultaneously master two disparate capabilities: creative narrative construction and rigid format adherence. The resulting outputs may mimic superficial style but lack the deep structural integrity and storytelling substance required for professional use. To enable LLMs to generate high-quality screenplays, we introduce Dual-Stage Refinement (DSR), a decomposed framework that decouples creative narrative generation from format conversion. The first stage transforms a brief outline into rich, novel-style prose. The second stage refines this narrative into a professionally formatted screenplay. This separation enables the model to specialize in one distinct capability at each stage. A key challenge in implementing DSR is the scarcity of paired outline-to-novel training data. We address this through hybrid data synthesis: reverse synthesis deconstructs existing screenplays into structured inputs, while forward synthesis leverages these inputs to generate high-quality narrative texts as training targets. Blind evaluations by professional screenwriters show that DSR achieves a 75% win rate against strong baselines like Gemini-2.5-Pro and reaches 82.7% of human-level performance. Our work demonstrates that decomposed generation architecture with tailored data synthesis effectively specializes LLMs in complex creative domains.

翻译：剧本是影视制作的基础，定义了叙事结构、角色发展与对白。尽管大语言模型在创意写作中展现出巨大潜力，但直接的端到端生成方法往往难以产出精心打磨的剧本。我们认为这种失败源于强制单一模型同时掌握两种截然不同的能力：创意叙事构建与严格格式遵循。由此产生的输出可能模仿了表面风格，却缺乏专业应用所需的深层结构完整性与叙事实质。为使大语言模型能够生成高质量剧本，我们提出了双阶段精炼框架——一种将创意叙事生成与格式转换解耦的分解式框架。第一阶段将简要大纲转化为丰富的类小说体散文；第二阶段则将此叙事精炼为专业格式的剧本。这种分离使模型能在每个阶段专注于一项特定能力。实施双阶段精炼框架的关键挑战在于配对式大纲到小说训练数据的稀缺性。我们通过混合数据合成方法解决此问题：逆向合成将现有剧本解构为结构化输入，而正向合成则利用这些输入生成高质量叙事文本作为训练目标。专业编剧的盲评结果显示，双阶段精炼框架相较于Gemini-2.5-Pro等强基线模型获得75%的胜率，并达到人类水平表现的82.7%。我们的研究表明，结合定制化数据合成的分解式生成架构能有效提升大语言模型在复杂创意领域的专项能力。