DPWriter: 基于多样化规划分支的强化学习用于创造性写作
DPWriter: Reinforcement Learning with Diverse Planning Branching for Creative Writing
AI总结：
DPWriter通过多样化规划分支方法提升创造性写作的输出多样性，同时保持生成质量。
摘要：
基于强化学习（RL）的大型语言模型（LLMs）增强通常会导致输出多样性降低，这削弱了它们在开放性任务如创造性写作中的实用性。当前方法缺乏明确的机制来引导多样性的探索，而是优先考虑优化效率和性能而非多样性。本文提出了一种基于半结构化长链式思考（CoT）的RL框架，其中生成过程被分解为显式规划的中间步骤。我们引入了一种多样化规划分支方法，根据多样性变化在规划阶段战略性地引入分歧，同时引入群体意识的多样性奖励以鼓励不同的轨迹。在创造性写作基准上的实验结果表明，我们的方法在不牺牲生成质量的情况下显著提高了输出多样性，一致优于现有基线。
英文摘要：
Reinforcement learning (RL)-based enhancement of large language models (LLMs) often leads to reduced output diversity, undermining their utility in open-ended tasks like creative writing. Current methods lack explicit mechanisms for guiding diverse exploration and instead prioritize optimization efficiency and performance over diversity. This paper proposes an RL framework structured around a semi-structured long Chain-of-Thought (CoT), in which the generation process is decomposed into explicitly planned intermediate steps. We introduce a Diverse Planning Branching method that strategically introduces divergence at the planning phase based on diversity variation, alongside a group-aware diversity reward to encourage distinct trajectories. Experimental results on creative writing benchmarks demonstrate that our approach significantly improves output diversity without compromising generation quality, consistently outperforming existing baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
