Agent0: Unleashing Self-Evolving Agents from Zero Data via Tool-Integrated Reasoning
Peng Xia, Kaide Zeng, Jiaqi Liu, Can Qin, Fang Wu, Yiyang Zhou, Caiming Xiong, Huaxiu Yao

TL;DR
Agent0 introduces a fully autonomous, self-evolving framework for LLM agents that iteratively develops complex curricula and enhances reasoning abilities without external data, leveraging tool integration and multi-step co-evolution.
Contribution
It presents a novel self-evolution approach for LLM agents that eliminates the need for external data and enables complex reasoning and tool use through iterative co-evolution.
Findings
Improves reasoning benchmarks by 18% on mathematical tasks.
Enhances general reasoning performance by 24%.
Demonstrates effective self-driven curriculum generation.
Abstract
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…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Topic Modeling · Reinforcement Learning in Robotics
