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Tom Everitt

Tom Everitt has 2 papers in our library, in 2018–2021; often with Geoffrey Irving, Iason Gabriel, Jan Leike.

2 papers in our library · 2018–2021

OpenAlex · ORCID · Wikidata · h-index 14 · 71 works · 1,009 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 189 times in our library · ⭐ Paper Stars ranking: #5202

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🏢Google DeepMindLondon · United Kingdom2020–2026 · 22 papersEmploymentcurrent🏢GoogleMountain View · United States2019–2026 · 5 paperscurrent🎓Australian National UniversityCanberra · Australia2015–2022 · 19 papersEducation since 2015🏢GoogleLondon · United Kingdom2021–2022 · 2 papers🎓Stockholm UniversityStockholm · Sweden2011–2016 · 2 papersEducation 2011–2013Employment 2013–2015

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksComputer Science · 2 worksArts and Humanities · 1 workLanguage and Linguistics · 1 workSafety Research · 1 workSocial Sciences · 1 work

 Works by year

2020s

20211 paper

2010s

20181 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Scalable agent alignment via reward modeling: a research direction (2018 · 227 citations)
    Reinforcement learning thrives when a task has an explicit score, as in Atari or Go. Most practical jobs, such as handling email, writing software or managing a business, offer no built-in reward, and the user's goal exists only implicitly. The authors call this the agent alignment problem: making an agent act as its…
  2. Alignment of Language Agents (2021 · 120 citations)
    This DeepMind position paper asks how systems that act only by producing text, which the authors call language agents, can end up behaving against their designers' intentions.

 Co-authors here

7 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 189 times (of the 149,439 papers whose bibliographies we read); 72 citing papers with a page here →.

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