ReAct: Synergizing Reasoning and Acting in…

ReAct gives language-model agents a simple working rhythm: reason about the current situation, take a bounded action, observe what the environment returns, and revise the next move. Read it for the durable idea behind many tool-using agent harnesses and for an honest look at why better grounding can still fail when tools return weak evidence or the agent gets stuck in a loop.

Reading focus: How ReAct interleaves language thoughts, task-specific actions, and returned observations in one inspectable trajectory. Why thoughts can decompose goals and recover from surprises while actions ground later reasoning in new external evidence. Why tool quality, prompt choice, step limits, repetition detection, and narrow permissions remain part of a reliable agent harness.

ICLR 2023. Yao, Zhao, Yu, Du, Shafran, Narasimhan, and Cao. 35 min read, easy difficulty.