LongWriter: Unleashing 10,000+ Word Generation from Long Context LLMs
Authors: name: Yushi Bai, name: Jiajie Zhang, name: Xin Lv, name: Linzhi Zheng, name: Siqi Zhu, name: Lei Hou, name: Yuxiao Dong, name: Jie Tang, name: Juanzi Li

Current long context large language models (LLMs) can process inputs up to
100,000 tokens, yet struggle to generate outputs exceeding even a modest length
of 2,000 words. Through controlled experiments, we find that the model's
effective generation length is inherently bounded by the sample it has seen
during supervised fine-tuning (SFT). In other words, their output limitation is
due to the scarcity of long-output examples in existing SFT datasets. To
address this, we introduce AgentWrite, an agent-based pipeline that decomposes
ultra-long generation tasks into subtasks, enabling off-the-shelf LLMs to
generate coherent outputs exceeding 20,000 words. Leveraging AgentWrite, we
construct LongWriter-6k, a dataset containing 6,000 SFT data with output
lengths ranging from 2k to 32k words. By incorporating this dataset into model
training, we successfully scale the output length of existing models to over
10,000 words while maintaining output quality. We also develop LongBench-Write,
a comprehensive benchmark for evaluating ultra-long generation capabilities.
Our 9B parameter model, further improved through DPO, achieves state-of-the-art
performance on this benchmark, surpassing even much larger proprietary models.
In general, our work demonstrates that existing long context LLM already
possesses the potential for a larger output window--all you need is data with
extended output during model alignment to unlock this capability. Our code &
models are at: https://github.com/THUDM/LongWriter.