Researchers at Alibaba Group have developed a novel approach that could dramatically reduce the cost and complexity of training AI systems to search for information, eliminating the need for expensive commercial search engine APIs altogether. The technique, called “ZeroSearch,” allows large language models (LLMs) to develop advanced search capabilities through a simulation approach rather than interacting with real search engines during the training process. This innovation could save companies significant API expenses while offering better control over how AI systems learn to retrieve information. “Reinforcement learning [RL] training requires frequent rollouts, potentially involving hundreds of thousands of search requests, which incur substantial API expenses and severely constrain scalability,” write the researchers in their paper published on arXiv this week. “To address these challenges, we introduce ZeroSearch, a reinforcement learning framework that incentivizes the search capabilities of LLMs without interacting with real search engines.”

Full report : Alibaba researchers detail ZeroSearch, a technique that allows LLMs to develop advanced search capabilities via simulation, claiming it cuts costs by up to 88%.