Language Models can Self-Lengthen to Generate Long Texts
Authors: name: Shanghaoran Quan, name: Tianyi Tang, name: Bowen Yu, name: An Yang, name: Dayiheng Liu, name: Bofei Gao, name: Jianhong Tu, name: Yichang Zhang, name: Jingren Zhou, name: Junyang Lin

Recent advancements in Large Language Models (LLMs) have significantly
enhanced their ability to process long contexts, yet a notable gap remains in
generating long, aligned outputs. This limitation stems from a training gap
where pre-training lacks effective instructions for long-text generation, and
post-training data primarily consists of short query-response pairs. Current
approaches, such as instruction backtranslation and behavior imitation, face
challenges including data quality, copyright issues, and constraints on
proprietary model usage. In this paper, we introduce an innovative iterative
training framework called Self-Lengthen that leverages only the intrinsic
knowledge and skills of LLMs without the need for auxiliary data or proprietary
models. The framework consists of two roles: the Generator and the Extender.
The Generator produces the initial response, which is then split and expanded
by the Extender. This process results in a new, longer response, which is used
to train both the Generator and the Extender iteratively. Through this process,
the models are progressively trained to handle increasingly longer responses.
Experiments on benchmarks and human evaluations show that Self-Lengthen
outperforms existing methods in long-text generation, when applied to top
open-source LLMs such as Qwen2 and LLaMA3. Our code is publicly available at
https://github.com/QwenLM/Self-Lengthen.