Agent Series (2): ReAct — The Most Important Agent Reasoning Paradigm
The article discusses the ReAct paradigm, which enhances agent reasoning by interleaving reasoning and acting. Unlike traditional methods that rely solely on training data, ReAct allows models to interact with real-world data, improving accuracy. This approach creates a dynamic execution path that adapts based on actual observations.
- ▪ReAct was developed to address the limitations of traditional agent reasoning methods like Chain-of-Thought.
- ▪The ReAct process involves a loop of Thought, Action, and Observation, allowing models to update their reasoning based on real feedback.
- ▪A demo illustrates ReAct's capabilities, showing how an agent can dynamically decide its actions based on search results.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3797373) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } WonderLab Posted on May 23 Agent Series (2): ReAct — The Most Important Agent Reasoning Paradigm #llm #ai #langchain #agents You Think Your Agent Is "Thinking." It's Actually Just Predicting Tokens. Here's a scenario that happens more often than you'd think. You ask an Agent to write a competitive analysis report. It confidently outputs three professional-looking pages — complete with data, conclusions, and strategic recommendations.
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