ReAct: Teaching LLMs to Think, Then Act
ReAct teaches LLMs to 'think then do,' interleaving reasoning steps with actions like querying a database. Instead of just generating a final answer, the model forms a thought, acts on it, observes the result, and then thinks again. This is crucial for complex question-answering where the model must gather external information to ground its reasoning. The main footgun it avoids is hallucination, where models invent facts instead of looking them up.
ReAct gives language models a powerful loop: think, act, observe. Instead of attempting a complex task in one go, the model generates an internal thought about what to do next, then an action to execute (like searching a knowledge base), and finally observes the result to inform its next thought. This pattern is highly effective for multi-step tasks requiring external knowledge, like question answering where a model can query a Wikipedia API to find a specific fact and avoid hallucination. It's also used for agents navigating websites. The primary footgun is treating LLMs as pure reasoning engines; without ReAct's external actions, chain-of-thought reasoning can easily propagate an early error, leading to confidently wrong answers.
Read the original → arXiv
- #llm
- #agent
- #generative ai
- #reasoning
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