Beyond RAG: SEARCH-R1 integrates search engines directly into reasoning models
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Summary
A new technique called SEARCH-R1 trains large language models (LLMs) to generate search queries and seamlessly integrate search engine results into their reasoning processes.
It allows the model to invoke a search engine multiple times while it is reasoning about a problem and obtaining new information, making it more dynamic and responsive.
The technique uses reinforcement learning to train LLMs to interleave search queries with their reasoning chains, rewarding them based on the correctness of the final response rather than on complex reward models that verify the model’s reasoning process.
SEARCH-R1 could be useful in customer support, knowledge management and data analysis, according to the researchers from the University of Illinois at Urbana-Champaign and the University of Massachusetts Amherst, who released the code on GitHub.