This paper describes the Self-Challenging framework, a method for training large language model (LLM) agents to use tools by generating their own training tasks. The framework involves the agent acting as a "challenger" to create tasks and then as an "executor" to solve them using reinforcement learning. To ensure task quality, the paper introduces the "Code-as-Task" (CaT) formalism, where tasks are defined by an instruction, a verifiable code function, an example solution, and failure cases. Experiments on existing benchmarks show that this self-generated training data significantly improves the performance of the LLM agent, highlighting the potential for autonomous agent improvement.