ReAct: Synergizing Reasoning and Acting in Language Models
Source
- Person key:
ysymyth - Source kind:
paper - Canonical URL: https://arxiv.org/abs/2210.03629
- License:
NOASSERTION - Public handling:
public-metadata-summary-hash-link-only - Semantic hash:
c29aff9c7c0d81a00bc829001f0df2e25699fafe93baa58e587bfa27513a35c5 - First seen: 2026-05-16
- Last changed: 2026-05-16
- Identity guard: Do not confuse with yao-shunyu-alfred, the physics-to-AI researcher at alfredyao.github.io.
Classification
- Category: Language agents / agent architectures
- Topic hub: shunyu-yao-public-corpora
- Project taxonomy: shunyu-yao-project-taxonomy
- Paper map: shunyu-yao-paper-map
Summary
While large language models (LLMs) have demonstrated impressive capabilities across tasks in language understanding and interactive decision making, their abilities for reasoning (e.g. chain-of-thought prompting) and acting (e.g. action plan generation) have primarily been studied as separate topics. In this paper, we explore the use of LLMs to generate both reasoning traces and task-specific actions in an interleaved manner, allowing for greater synergy between the two: reasoning traces help the model induce, trac…
What This Teaches
How language models become agents through reasoning, acting, memory, tools, and interface design.
Related
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