Saturday, 31 October 2026 · Weekend classic from 2022 · 5 min read
ReAct combines reasoning and acting in language models for better task solving
ReAct prompts large language models to interleave reasoning traces and task-specific actions, improving performance and interpretability on knowledge-intensive QA and interactive decision-making tasks, outperforming baselines with few-shot prompting.
Based on the paper ReAct: Synergizing Reasoning and Acting in Language Models by Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du and 3 others
Challenges in combining reasoning and acting in language models
Large language models (LLMs) have shown strong abilities in reasoning tasks like chain-of-thought prompting and in acting tasks such as generating action plans, but these capabilities have mostly been studied separately.
Reasoning-only approaches generate internal thought chains without interacting with external information, leading to hallucinations and error propagation, while acting-only approaches generate actions without explicit reasoning, which can cause failures in complex tasks requiring planning or knowledge updates.
Integrating reasoning and acting is challenging because it requires the model to maintain and update plans, handle exceptions, and gather external information dynamically, all within a coherent framework.
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