ZeroSearch is a novel reinforcement learning framework designed to incentivize the search capabilities of large language models (LLMs) without interacting with actual search engines. Through supervised fine-tuning, ZeroSearch transforms LLMs into retrieval modules capable of generating relevant and irrelevant documents, and introduces a curriculum rollout mechanism to gradually enhance the model's reasoning ability. The main advantage of this technology lies in its superior performance compared to models based on real search engines, while incurring zero API costs. It is suitable for LLMs of all sizes and supports various reinforcement learning algorithms, making it ideal for research and development teams that require efficient retrieval capabilities.