Tool-R0: Self-Evolving LLM Agents for Tool-Learning from Zero Data
arXiv:2602.21320v1 Announce Type: new Abstract: Large language models (LLMs) are becoming the foundation for autonomous agents that can use tools to solve complex tasks. Reinforcement learning (RL) has emerged as a common approach for injecting such agentic capabilities, but typically under tightly controlled training setups. It often depends on carefully constructed task-solution pairs and substantial human supervision, which creates a fundamental obstacle to open-ended self-evolution toward superintelligent systems. In this paper, we propose Tool-R0 framework for training general purpose tool-calling agents from scratch with self-play RL, under a zero-data assumption. Initialized from the same base LLM, Tool-R0 co-evolves a Generator and a Solver with complementary rewards: one proposes targeted challenging tasks at the other's competence frontier and the other learns to solve them with real-world tool calls. This creates a self-evolving cycle that requires no pre-existing tasks or
arXiv:2602.21320v1 Announce Type: new Abstract: Large language models (LLMs) are becoming the foundation for autonomous agents that can use tools to solve complex tasks. Reinforcement learning (RL) has emerged as a common approach for injecting such agentic capabilities, but typically under tightly controlled training setups. It often depends on carefully constructed task-solution pairs and substantial human supervision, which creates a fundamental obstacle to open-ended self-evolution toward superintelligent systems. In this paper, we propose Tool-R0 framework for training general purpose tool-calling agents from scratch with self-play RL, under a zero-data assumption. Initialized from the same base LLM, Tool-R0 co-evolves a Generator and a Solver with complementary rewards: one proposes targeted challenging tasks at the other's competence frontier and the other learns to solve them with real-world tool calls. This creates a self-evolving cycle that requires no pre-existing tasks or datasets. Evaluation on different tool-use benchmarks show that Tool-R0 yields 92.5 relative improvement over the base model and surpasses fully supervised tool-calling baselines under the same setting. Our work further provides empirical insights into self-play LLM agents by analyzing co-evolution, curriculum dynamics, and scaling behavior.
Executive Summary
The article 'Tool-R0: Self-Evolving LLM Agents for Tool-Learning from Zero Data' proposes a novel framework for training large language models (LLMs) to use tools and solve complex tasks with self-play reinforcement learning (RL). The Tool-R0 framework co-evolves a Generator and a Solver, allowing the model to learn from scratch without pre-existing tasks or datasets. The authors demonstrate significant improvements in tool-use benchmarks, surpassing fully supervised tool-calling baselines. This breakthrough has far-reaching implications for autonomous agents and artificial intelligence, particularly in domains where human supervision is scarce or impractical. The article provides valuable insights into self-play LLM agents and their potential applications.
Key Points
- ▸ Tool-R0 framework enables self-evolving LLM agents to learn tool-use from scratch with self-play RL
- ▸ Co-evolution of Generator and Solver allows for targeted challenging tasks and real-world tool calls
- ▸ Significant improvements in tool-use benchmarks and surpassing of fully supervised tool-calling baselines
Merits
Strength
The article addresses a critical limitation of previous RL approaches, which often rely on carefully constructed task-solution pairs and human supervision.
Demerits
Limitation
The authors do not explore the potential risks and challenges associated with self-evolving LLM agents, such as the development of unpredictable or undesirable behaviors.
Expert Commentary
The Tool-R0 framework is a significant contribution to the field of artificial intelligence, as it addresses a long-standing challenge in RL research. However, the authors should be commended for acknowledging the potential risks and limitations of their approach. As we continue to develop more advanced AI systems, it is essential to carefully consider the potential consequences and implications of such technologies. The Tool-R0 framework has the potential to revolutionize various domains, but it also requires careful evaluation and regulation to ensure that its benefits are realized while minimizing its risks.
Recommendations
- ✓ Future research should focus on exploring the potential risks and challenges associated with self-evolving LLM agents and developing strategies to mitigate them.
- ✓ The development of the Tool-R0 framework should be accompanied by a thorough evaluation of its potential applications, benefits, and limitations, as well as a careful consideration of the policy and regulatory implications.
