Agent0：通过工具集成推理实现从零数据中自我进化的智能体

摘要

大型语言模型（LLM）智能体通常通过强化学习进行训练，但其发展受限于对人类标注数据的依赖，这不仅制约了扩展性，还将人工智能束缚于人类知识范畴。现有自进化框架虽提供替代方案，但往往受限于模型固有能力和单轮交互机制，难以支撑涉及工具使用或动态推理的复杂课程学习。我们提出Agent0——一种完全自主的框架，通过多步协同进化与无缝工具集成，无需外部数据即可培育高性能智能体。该框架在源自同一基础LLM的两个智能体间建立共生竞争机制：课程智能体负责提出日益挑战性的前沿任务，执行智能体则学习解决这些任务。通过集成外部工具增强执行者的问题解决能力，这种提升反过来迫使课程智能体构建更复杂的工具感知任务。在此迭代过程中，Agent0形成了自我强化的循环，持续生成高质量课程体系。实验表明，Agent0显著提升推理能力，将Qwen3-8B-Base模型在数学推理和通用推理基准上的表现分别提升18%和24%。代码已开源：https://github.com/aiming-lab/Agent0。

English

Large Language Model (LLM) Agents, often trained with Reinforcement Learning (RL), are constrained by a dependency on human-curated data, limiting scalability and tethering AI to human knowledge. Existing self-evolution frameworks offer an alternative but are typically restricted by the model's inherent capabilities and single-round interactions, hindering the development of complex curricula involving tool use or dynamic reasoning. We introduce Agent0, a fully autonomous framework that evolves high-performing agents without external data through multi-step co-evolution and seamless tool integration. Agent0 establishes a symbiotic competition between two agents initialized from the same base LLM: a curriculum agent that proposes increasingly challenging frontier tasks, and an executor agent that learns to solve them. We integrate external tools to enhance the executor's problem-solving capacity; this improvement, in turn, pressures the curriculum agent to construct more complex, tool-aware tasks. Through this iterative process, Agent0 establishes a self-reinforcing cycle that continuously produces high-quality curricula. Empirically, Agent0 substantially boosts reasoning capabilities, improving the Qwen3-8B-Base model by 18% on mathematical reasoning and 24% on general reasoning benchmarks. Code is available at https://github.com/aiming-lab/Agent0.

Agent0：通过工具集成推理实现从零数据中自我进化的智能体

Agent0: Unleashing Self-Evolving Agents from Zero Data via Tool-Integrated Reasoning

摘要

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