LongWriter-Zero: Mastering Ultra-Long Text Generation via Reinforcement
  Learning
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
685a0f330e4ad7e219758514
Yuhao Wu
685a0f330e4ad7e219758515
Yushi Bai
claimed_verified
2025-06-25T08:09:23.426Z
64ed568ccf6118a9379a61b8
/avatars/6d040cbcb4a9b624cbe64c9d01cd5c88.svg
Yushi Bai
user
bys0318
685a0f330e4ad7e219758516
Zhiqiang Hu
claimed_verified
2025-06-24T08:08:35.954Z
637f228152229c63921119c3
https://cdn-avatars.huggingface.co/v1/production/uploads/637f228152229c63921119c3/acwXorra1r9_7i3KlBFjS.jpeg
Zhiqiang Hu
user
Zhiqiang007
685a0f330e4ad7e219758517
Roy Ka-Wei Lee
685a0f330e4ad7e219758518
Juanzi Li
LongWriter-Zero: Mastering Ultra-Long Text Generation via Reinforcement
  Learning
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
An incentivization-based reinforcement learning approach is used to develop a large language model capable of generating ultra-long, high-quality text without the need for synthetic data or supervised fine-tuning.
reinforcement learning reward models long-form text generation ultra-long generation large language models synthetic fine-tuning length control writing quality structural formatting WritingBench Arena-Write