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

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 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.