ReAct: Synergizing Reasoning and Acting in Language Models

A frozen LLM that thinks, acts, and reads results in one loop — the blueprint for every agent.

Yao et al. · ICLR 2023 · Reasoning & RL. Read the paper ↗

A free, interactive, animated visual explainer of ReAct: Synergizing Reasoning and Acting in Language Models — every exhibit computed from the real formulas, with verbatim quotes from the source.

Questions

What is ReAct: Synergizing Reasoning and Acting in Language Models?

A frozen LLM that thinks, acts, and reads results in one loop — the blueprint for every agent.

Who published ReAct: Synergizing Reasoning and Acting in Language Models, and where?

Yao et al. — ICLR 2023 (arXiv:2210.03629).

Where can I find a visual explainer of ReAct: Synergizing Reasoning and Acting in Language Models?

Right here — a free, interactive, animated walkthrough of the whole paper, with exhibits computed from the real formulas and verbatim quotes from the source.

Related explainers

• DeepSeek-R1
• Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
• Training language models to follow instructions with human feedback
• Direct Preference Optimization: Your Language Model is Secretly a Reward Model
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
• Constitutional AI: Harmlessness from AI Feedback
• DAPO: An Open-Source LLM Reinforcement Learning System at Scale