Alibaba Group researchers have developed "ZeroSearch," a technique that enables large language models to [1]acquire search capabilities without using external search engines during training. The approach transforms LLMs into retrieval modules through supervised fine-tuning and employs a "curriculum-based rollout strategy" that gradually degrades generated document quality.

In tests across seven question-answering datasets, ZeroSearch [2]matched or exceeded the performance [PDF] of models trained with real search engines. A 7B-parameter retrieval module achieved results comparable to Google Search, while a 14B-parameter version outperformed it. The cost savings are substantial: training with 64,000 search queries using Google Search via SerpAPI would cost approximately $586.70, compared to just $70.80 using a 14B-parameter simulation LLM on four A100 GPUs -- an 88% reduction.

The technique works with multiple model families including Qwen-2.5 and LLaMA-3.2. Researchers have released their code, datasets, and pre-trained models on GitHub and Hugging Face, potentially lowering barriers to entry for smaller AI companies developing sophisticated assistants.



[1] https://venturebeat.com/ai/alibabas-zerosearch-lets-ai-learn-to-google-itself-slashing-training-costs-by-88-percent/

[2] https://arxiv.org/pdf/2505.04588