EconPapers    
Economics at your fingertips  
 

AI can learn to show its workings through trial and error

Daphne Ippolito () and Yiming Zhang ()

Nature, 2025, vol. 645, issue 8081, 594-595

Abstract: Large language models (LLMs) are more accurate when they output intermediate steps. A strategy called reinforcement can teach them to do this without being told.

Keywords: Machine learning; Computer science (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/d41586-025-02703-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:645:y:2025:i:8081:d:10.1038_d41586-025-02703-7

Ordering information: This journal article can be ordered from
https://www.nature.com/

DOI: 10.1038/d41586-025-02703-7

Access Statistics for this article

Nature is currently edited by Magdalena Skipper

More articles in Nature from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-09-19
Handle: RePEc:nat:nature:v:645:y:2025:i:8081:d:10.1038_d41586-025-02703-7