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LongZero: Enhancing Long-Context Reasoning in LLMs from Zero Data

Zhennan Wan, Jianfei Chen · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.30132 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

强化学习(RL)数据是加强大语言模型(LLM)长期背景推理的有效方法,但它需要长、多样、具有挑战性和可验证的培训数据。人类对这些数据的注释是昂贵的,自然发生的数据过于稀缺,以维持RL,并且现有的合成方法依赖于外部的长体或更强大的教师模型,这些模型随着训练的外部模型变得更难获得。最近,RL从零数据中学习,其中模型从自己提出的任务中学习,已经取得了卓越的表现,但现有的方法针对短背景任务。因此,我们提出了$textbf{LongZero}$,这是一种方法,允许LLM改进其自身的长背景推理,而无需外部数据。而不是在现有文档上写问题

Machine translation (Meta M2M100); the English original follows.

Abstract

Reinforcement learning (RL) is an effective way to strengthen the long-context reasoning of large language models (LLMs), but it needs training data that are long, diverse, challenging, and verifiable. Human annotation of such data is expensive, naturally occurring data are too scarce to sustain RL, and existing synthesis methods depend on external corpora or stronger teacher models, which become harder to obtain as the trained models get stronger. Recently, RL from zero data, in which a model learns from tasks it proposes itself, has achieved excellent performance, but existing methods target short-context tasks. Therefore, we propose $\textbf{LongZero}$, a method that lets an LLM improve its own long-context reasoning without any external data. Instead of writing questions and answers over existing documents, LongZero first has the model synthesize the question and answer with its supporting evidence in a compact structured form, and only then separately expands the evidence into a long multi-document context. LongZero combines these self-written multi-hop reasoning tasks with self-written multi-round retrieval tasks and trains the model with GRPO. We evaluate LongZero on two LLMs across five long-context reasoning benchmarks and compare it with RL on data synthesized from external corpora. Experimental results show that LongZero can generalize to real tasks, and outperforms both baselines, suggesting a viable and promising path to improving long-context reasoning of LLMs.

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Topics: Computer science, Artificial intelligence, Annotation, Path (computing), Reinforcement learning, Zero (linguistics)

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