拜拜，昂贵的谷歌搜索 API！阿里开源 RL 框架让大模型自给自足、成本直降88%，网友：游戏规则变了

Core Viewpoint - Alibaba's new technology "ZeroSearch" significantly reduces the cost and complexity of training AI systems for information retrieval, eliminating the need for expensive commercial search engine APIs [1][2][14]. Summary by Sections Technology Overview - ZeroSearch is a reinforcement learning framework that allows large language models (LLMs) to develop advanced search capabilities through simulation, outperforming models based on real search engines while incurring zero API costs [2][3]. - The technology is compatible with various model series, including Qwen-2.5 and LLaMA-3.2, and does not require a separate supervised preheating phase [2][3]. Performance Metrics - In comprehensive experiments across seven question-answer datasets, ZeroSearch's performance matched or exceeded that of models trained with real search engines [3][5]. - A 3 billion parameter LLM can achieve search capabilities comparable to Google, while a 14 billion parameter module can surpass Google's performance [3][5]. Cost Efficiency - Training using Google search via SerpAPI for approximately 64,000 queries costs around $586.70, while using a 14 billion parameter simulated LLM on four A100 GPUs costs only $70.80, representing an 88% reduction in costs [7][8]. Methodology - ZeroSearch begins with a lightweight supervised fine-tuning process that transforms LLMs into retrieval modules capable of generating relevant and irrelevant documents in response to queries [9][11]. - The system employs a course-based learning deployment mechanism, gradually increasing the difficulty of generated documents to simulate challenging retrieval scenarios [11][12]. Implications for AI Development - ZeroSearch represents a significant shift in AI training methods, enabling AI systems to improve without relying on external tools like search engines [14][15]. - This technology creates a more equitable competitive environment for small AI companies and startups by drastically lowering the entry barrier associated with high API costs [14][15].