The innovation of SimpleDeepSearcher is mainly inThree technical dimensionsDifferentiation from traditional RAG:
- Behavioral simulation mechanisms: Real-time information is obtained through a dynamic web search API that simulates the search-reasoning path generation process of real users, whereas RAGs typically rely on static knowledge bases
- Training methodology: AdoptionDouble distillation technology(Knowledge distillation + self-distillation), replacing the large amount of interaction data required by RL methods with selected data, and improving fine-tuning efficiency by 5-8 times
- Architecture CompatibilitySupport mainstream LLM such as QWEN2.5-32B model plug-and-play, no need for cold-start command fine-tuning
Practical tests show that in complex problem reasoning tasks, SimpleDeepSearcher's answer accuracy is 17.31 TP3T higher than the standard RAG, and the reasoning path is more in line with human thinking habits.
This answer comes from the articleSimpleDeepSearcher: An Intelligent Retrieval Tool for Augmenting Large Language Models with Web SearchThe
May not be reproduced without permission:AI productivity tools " What are the technical advantages of SimpleDeepSearcher over traditional retrieval augmentation methods (RAG)?
