Letting a language model think out loud and use tools
orig. “ReAct: Synergizing Reasoning and Acting in Language Models” · Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, Yuan Cao
This paper lets a model switch between reasoning step by step and taking actions, like looking something up, so it can solve tasks it could not before.
A plain language model answers in one shot and cannot check facts. ReAct has the model interleave two things: a short thought about what to do next, and an action such as searching a database or a website. It reads the result, thinks again, and continues until it has an answer. This loop lets the model gather information it did not memorise and correct itself along the way.
This think-and-act loop is the backbone of modern AI agents, the systems that browse, use tools, and complete multi-step tasks. It made models far more useful for real work than answering from memory alone. If you have seen an AI search the web and reason about what it finds, this is the idea underneath.
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, Yuan Cao, Princeton University and Google Research
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