LongWriter-Zero: Mastering Ultra-Long Text Generation via Reinforcement
  Learning
Authors: name: Yuhao Wu, name: Yushi Bai, name: Zhiqiang Hu, name: Roy Ka-Wei Lee, name: Juanzi Li

Ultra-long generation by large language models (LLMs) is a widely demanded
scenario, yet it remains a significant challenge due to their maximum
generation length limit and overall quality degradation as sequence length
increases. Previous approaches, exemplified by LongWriter, typically rely on
''teaching'', which involves supervised fine-tuning (SFT) on synthetic
long-form outputs. However, this strategy heavily depends on synthetic SFT
data, which is difficult and costly to construct, often lacks coherence and
consistency, and tends to be overly artificial and structurally monotonous. In
this work, we propose an incentivization-based approach that, starting entirely
from scratch and without relying on any annotated or synthetic data, leverages
reinforcement learning (RL) to foster the emergence of ultra-long, high-quality
text generation capabilities in LLMs. We perform RL training starting from a
base model, similar to R1-Zero, guiding it to engage in reasoning that
facilitates planning and refinement during the writing process. To support
this, we employ specialized reward models that steer the LLM towards improved
length control, writing quality, and structural formatting. Experimental
evaluations show that our LongWriter-Zero model, trained from Qwen2.5-32B,
consistently outperforms traditional SFT methods on long-form writing tasks,
achieving state-of-the-art results across all metrics on WritingBench and
Arena-Write, and even surpassing 100B+ models such as DeepSeek R1 and
Qwen3-235B. We open-source our data and model checkpoints under
https://huggingface.co/THU-KEG/LongWriter-Zero-32B