title: SuperWriter: Reflection-Driven Long-Form Generation with Large Language
    Models
summary: Long-form text generation remains a significant challenge for large language
  models (LLMs), particularly in maintaining coherence, ensuring logical
  consistency, and preserving text quality as sequence length increases. To
  address these limitations, we propose SuperWriter-Agent, an agent-based
  framework designed to enhance the quality and consistency of long-form text
  generation. SuperWriter-Agent introduces explicit structured thinking-through
  planning and refinement stages into the generation pipeline, guiding the model
  to follow a more deliberate and cognitively grounded process akin to that of a
  professional writer. Based on this framework, we construct a supervised
  fine-tuning dataset to train a 7B SuperWriter-LM. We further develop a
  hierarchical Direct Preference Optimization (DPO) procedure that uses Monte
  Carlo Tree Search (MCTS) to propagate final quality assessments and optimize
  each generation step accordingly. Empirical results across diverse benchmarks
  demonstrate that SuperWriter-LM achieves state-of-the-art performance,
  surpassing even larger-scale baseline models in both automatic evaluation and
  human evaluation. Furthermore, comprehensive ablation studies demonstrate the
  effectiveness of hierarchical DPO and underscore the value of incorporating
  structured thinking steps to improve the quality of long-form text generation.
paper_authors._id: 6840fefb3098ab525906d852
paper_authors.name: Yuhao Wu
paper_authors._id: 6840fefb3098ab525906d853
paper_authors.name: Yushi Bai
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-06-05T12:46:26.352Z
paper_authors._id: 64ed568ccf6118a9379a61b8
paper_authors.avatarUrl: /avatars/6d040cbcb4a9b624cbe64c9d01cd5c88.svg
paper_authors.fullname: Yushi Bai
paper_authors.type: user
paper_authors.user: bys0318
paper_authors._id: 6840fefb3098ab525906d854
paper_authors.name: Zhiqiang Hu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-05T07:27:09.305Z
paper_authors._id: 637f228152229c63921119c3
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/637f228152229c63921119c3/acwXorra1r9_7i3KlBFjS.jpeg
paper_authors.fullname: Zhiqiang Hu
paper_authors.type: user
paper_authors.user: Zhiqiang007
paper_authors._id: 6840fefb3098ab525906d855
paper_authors.name: Juanzi Li
paper_authors._id: 6840fefb3098ab525906d856
paper_authors.name: Roy Ka-Wei Lee
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-06-05T12:46:33.575Z
paper_authors._id: 64b94cc0e3d41dbd6974ae45
paper_authors.avatarUrl: /avatars/5edb9d9465addfceccef04c4465a34e6.svg
paper_authors.fullname: Roy Ka-Wei lee
paper_authors.type: user
paper_authors.user: sroylee
paper_title: SuperWriter: Reflection-Driven Long-Form Generation with Large Language
    Models
paper_summary: Long-form text generation remains a significant challenge for large language
  models (LLMs), particularly in maintaining coherence, ensuring logical
  consistency, and preserving text quality as sequence length increases. To
  address these limitations, we propose SuperWriter-Agent, an agent-based
  framework designed to enhance the quality and consistency of long-form text
  generation. SuperWriter-Agent introduces explicit structured thinking-through
  planning and refinement stages into the generation pipeline, guiding the model
  to follow a more deliberate and cognitively grounded process akin to that of a
  professional writer. Based on this framework, we construct a supervised
  fine-tuning dataset to train a 7B SuperWriter-LM. We further develop a
  hierarchical Direct Preference Optimization (DPO) procedure that uses Monte
  Carlo Tree Search (MCTS) to propagate final quality assessments and optimize
  each generation step accordingly. Empirical results across diverse benchmarks
  demonstrate that SuperWriter-LM achieves state-of-the-art performance,
  surpassing even larger-scale baseline models in both automatic evaluation and
  human evaluation. Furthermore, comprehensive ablation studies demonstrate the
  effectiveness of hierarchical DPO and underscore the value of incorporating
  structured thinking steps to improve the quality of long-form text generation.
paper_ai_summary: SuperWriter-Agent enhances long-form text generation by integrating structured planning and refinement, achieving top performance with a 7B model and hierarchical Direct Preference Optimization.
paper_ai_keywords: agent-based framework structured thinking-through planning refinement stages SuperWriter-Agent SuperWriter-LM hierarchical Direct Preference Optimization Monte Carlo Tree Search DPO automatic evaluation human evaluation ablation studies