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[ICLR 2023] ReAct: Synergizing Reasoning and Acting in Language Models
| Date | Stars |
|---|---|
| 2026-07-31 | 4089 |
| 2026-08-01 | 4091 |
| 2026-08-03 | 4091 |
| 2026-08-06 | 4091 |
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# ReAct Prompting
GPT-3 prompting code for ICLR 2023 paper [ReAct: Synergizing Reasoning and Acting in Language Models](https://arxiv.org/abs/2210.03629).
To use ReAct for more tasks, consider trying [LangChain's zero-shot ReAct Agent](https://python.langchain.com/docs/modules/agents/agent_types/react.html).
## Setup
You need to first have an OpenAI API key and store it in the environment variable ``OPENAI_API_KEY`` (see [here](https://help.openai.com/en/articles/5112595-best-practices-for-api-key-safety)).
Package requirement: ``openai``, and install ``alfworld`` following instructions [here](https://github.com/alfworld/alfworld).
## Experiments
Run ``{hotpotqa,fever,alfworld,webshop}.ipynb``. As HotpotQA and FEVER have large validation sets, we only run 500 random examples (see notebooks). We find PaLM and GPT-3 are better at different tasks.
| | HotpotQA (500 random dev, EM) | FEVER (500 random dev, EM) | AlfWorld (success rate) | WebShop (success rate) |
|--------------------|-------------------------------|----------------------------|-------------------------|-------------------------|
| PaLM-540B (paper) | 29.4 | 62.2 | 70.9 | 40 |
| GPT-3 (davinci-002) | 30.4 | 54 | 78.4 | 35.8 |
## Citation
```bibtex
@inproceedings{yao2023react,
title = {{ReAct}: Synergizing Reasoning and Acting in Language Models},
author = {Yao, Shunyu and Zhao, Jeffrey and Yu, Dian and Du, Nan and Shafran, Izhak and Narasimhan, Karthik and Cao, Yuan},
booktitle = {International Conference on Learning Representations (ICLR) },
year = {2023},
html = {https://arxiv.org/abs/2210.03629},
}
```
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Shunyu Yao · Princeton University
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John Yang
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f6e8a8f00ff5d837, topic:large-language-models, topic:llm