# Tim Rocktäschel

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> Updated: 2026-10-27
> Categories: AI Agents, People, Reinforcement Learning
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**Tim Rocktäschel** is a German computer scientist known for his work on [reinforcement learning](https://aiwiki.ai/wiki/reinforcement_learning), open-ended learning, and language-based [AI agents](https://aiwiki.ai/wiki/ai_agents). He is a Professor of [Artificial Intelligence](https://aiwiki.ai/wiki/artificial_intelligence) at [University College London](https://aiwiki.ai/wiki/university_college_london) (UCL), where he leads the Deciding, Acting, and Reasoning with Knowledge (DARK) Lab, and until 2026 he was a Director and Principal Scientist at [Google DeepMind](https://aiwiki.ai/wiki/google_deepmind), where he led the Open-Endedness team [1][2]. He co-founded [Recursive Superintelligence](https://aiwiki.ai/wiki/recursive_superintelligence), a London- and San Francisco-based startup that aims to build self-improving AI and was incorporated in London in December 2025 [3][4][13]. His research output is widely cited; he is a co-author of the original [retrieval-augmented generation](https://aiwiki.ai/wiki/retrieval_augmented_generation) (RAG) paper and a co-creator of the [NetHack](https://aiwiki.ai/wiki/nethack_learning_environment) Learning Environment, and he shared two Best Paper Awards at the 2024 [International Conference on Machine Learning](https://aiwiki.ai/wiki/icml) [1][5][6].

## Quick facts

| Item | Detail |
| --- | --- |
| Born | Berlin, Germany |
| Field | [Artificial intelligence](https://aiwiki.ai/wiki/artificial_intelligence), [reinforcement learning](https://aiwiki.ai/wiki/reinforcement_learning), open-endedness [1] |
| Doctorate | PhD, Computer Science, University College London (2017) [1] |
| Doctoral advisor | [Sebastian Riedel](https://aiwiki.ai/wiki/sebastian_riedel) [1] |
| Current academic role | Professor of [Artificial Intelligence](https://aiwiki.ai/wiki/artificial_intelligence), UCL (since 2023) [1][2] |
| Notable industry role | Director / Principal Scientist and Open-Endedness Team Lead, [Google DeepMind](https://aiwiki.ai/wiki/google_deepmind) (2022 to 2026) [1] |
| Startup role | Co-founder and director, Recursive Superintelligence (incorporated December 2025) [3][4][13] |
| Best-known work | RAG, NetHack Learning Environment, Genie [5][6][7] |

## Education

Rocktäschel grew up in Berlin, where he completed his Abitur at the Heinrich-Hertz Oberschule in 2006 with advanced courses in mathematics and computer science [1]. He studied at the [Humboldt-Universität zu Berlin](https://aiwiki.ai/wiki/humboldt_university_of_berlin) from 2006 to 2012, earning a Diplom in Informatik (computer science), the German degree roughly equivalent to a master's, with a subsidiary field in psychology [1]. His diploma thesis on jointly extracting proteins and biomolecular events using factor graphs won a faculty award, and during this period he placed first in the SemEval 2013 DDIExtraction shared task, an early sign of his interest in extracting structured knowledge from text [1].

He moved to UCL for doctoral study, completing his PhD in computer science between 2013 and 2017 [1]. His thesis, titled "Combining Representation Learning with Logic for Language Processing," was supervised by Sebastian Riedel, with Daniel Tarlow and Thore Graepel as additional advisors [1]. The doctorate was supported by a [Microsoft Research](https://aiwiki.ai/wiki/microsoft_research) PhD Scholarship and, later, a Google PhD Fellowship in [natural language processing](https://aiwiki.ai/wiki/natural_language_processing) [1].

## Early career: Oxford and Meta AI

After his PhD, Rocktäschel spent 2017 and 2018 at the [University of Oxford](https://aiwiki.ai/wiki/university_of_oxford). He was a postdoctoral researcher in reinforcement learning, a Junior Research Fellow at Jesus College, and a Stipendiary Lecturer at Hertford College, where he tutored undergraduates in functional and imperative programming [1].

In August 2018 he joined Facebook AI Research (FAIR), the lab now part of [Meta AI](https://aiwiki.ai/wiki/meta_ai), in London, while simultaneously taking up a faculty post at UCL [1]. He rose from research scientist to lead the lab's reinforcement learning team, serving as a Research Manager and Area Lead before leaving in 2022 [1]. His time at FAIR coincided with several of his most-cited works, including the RAG paper and the NetHack Learning Environment, both published in 2020 [5][6].

## Google DeepMind and the Open-Endedness team

Rocktäschel joined Google DeepMind in May 2022 as a Senior Staff Research Scientist and lead of the Open-Endedness team [1]. He had previously interned at DeepMind in 2015 during his doctoral studies [1]. In November 2024 he was promoted to Director and Principal Scientist while continuing to lead the team [1].

The Open-Endedness team studied systems that can keep generating new tasks and improving indefinitely, rather than converging on a fixed objective. Rocktäschel has framed [open-endedness](https://aiwiki.ai/wiki/open-endedness) as a route toward [artificial general intelligence](https://aiwiki.ai/wiki/artificial_general_intelligence) and self-improvement, organized around three threads: training agents that set their own goals, building large-scale world models that supply agents with endless environments, and connecting these methods to [large language models](https://aiwiki.ai/wiki/large_language_model) so that AI can improve itself [2]. During this period he was a co-author of Genie, an 11-billion-parameter foundation [world model](https://aiwiki.ai/wiki/world_model) that learns to generate playable, action-controllable environments from unlabeled internet videos [7].

## University College London

Rocktäschel has held a UCL appointment continuously since 2018, when he started as an Assistant Professor (Lecturer) at the Centre for Artificial Intelligence in the Department of Computer Science [1]. He was promoted to Associate Professor in 2021 and to full Professor of Artificial Intelligence in 2023, giving his inaugural lecture in 2024 [1][8]. He is the principal investigator of the DARK Lab, whose name stands for Deciding, Acting, and Reasoning with Knowledge, and his group's work on agents, world models, and self-improvement mirrors the themes of his DeepMind research [2].

He has been recognized within the broader European research community as a Scholar and later a Fellow of the European Laboratory for Learning and Intelligent Systems ([ELLIS](https://aiwiki.ai/wiki/ellis)) [1]. He has also served the field as a senior area chair at ICML, [NeurIPS](https://aiwiki.ai/wiki/neurips), and ICLR, and helped organize a series of workshops on agent learning in open-endedness and on language in reinforcement learning [1].

## Recursive Superintelligence

Rocktäschel co-founded Recursive Superintelligence, a startup pursuing recursively self-improving AI [3][4]. Companies House records show that Recursive Superintelligence Ltd. was incorporated on 31 December 2025, with Rocktäschel appointed as its director on that date [13]. The company is led by [Richard Socher](https://aiwiki.ai/wiki/richard_socher), the former chief scientist at [Salesforce](https://aiwiki.ai/wiki/salesforce) and founder of the [search engine](https://aiwiki.ai/wiki/search_engine) You.com, as chief executive, with Rocktäschel as a co-founder drawing on his DeepMind background in open-endedness and world models [3][9]. Reporting placed its founding team, which also includes researchers who previously worked at [OpenAI](https://aiwiki.ai/wiki/openai), Meta, and Uber AI, as having come together over late 2025 and early 2026 [9].

In May 2026 the company emerged from stealth, having raised more than 650 million dollars at a valuation of about 4.65 billion dollars [3][4]. The round was led by GV (formerly Google Ventures) and Greycroft, with participation from the chipmakers [Nvidia](https://aiwiki.ai/wiki/nvidia) and [AMD](https://aiwiki.ai/wiki/amd) through their venture arms [3][4]. Recursive is incorporated in London with offices in London and San Francisco and a team of fewer than thirty people [4]. Its stated plan is to first build AI that improves AI and then extend the approach to other scientific fields, automating the cycle of generating, testing, and validating research ideas [3].

## Research

Rocktäschel's Google Scholar profile lists more than 42,000 citations and an h-index above 60, with a research focus tagged simply as open-endedness [5]. His work falls into a few broad areas.

### Neural reasoning and knowledge

His early work combined [neural network](https://aiwiki.ai/wiki/neural_network)s with logic and structured knowledge. "Reasoning about Entailment with Neural Attention" (ICLR 2016), written with Edward Grefenstette, Karl Moritz Hermann, Tomáš Kočiský, and Phil Blunsom, introduced a word-by-word neural attention mechanism for natural language inference and was an early, influential demonstration of attention for textual entailment [10]. "End-to-End Differentiable Proving" (NeurIPS 2017), an oral presentation, showed how to perform differentiable theorem proving by combining the structure of symbolic provers with learned vector representations of symbols [5]. He also contributed to "Language Models as Knowledge Bases?" (EMNLP 2019), which probed how much factual knowledge is stored in the parameters of pretrained [language model](https://aiwiki.ai/wiki/language_model)s [5].

### Retrieval-augmented generation

Rocktäschel is the tenth-listed author of "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" (NeurIPS 2020), the FAIR paper led by Patrick Lewis that introduced RAG [11]. RAG combines a pretrained sequence-to-sequence generator with a learned retriever over an external document index, fine-tuned end to end, and the approach became a standard technique for grounding language models in retrieved evidence [11]. It is by a wide margin his most-cited paper [5].

### Reinforcement learning environments and open-ended agents

He co-created the NetHack Learning Environment (NeurIPS 2020), a fast, procedurally generated reinforcement learning testbed built on the roguelike game NetHack [6]. The authors argued that NetHack is complex enough to drive long-term research on exploration, planning, and skill acquisition while remaining cheap to simulate, making it a useful benchmark for hard-exploration and open-ended learning [6]. He later helped author a related framework, MiniHack, for designing custom RL tasks on the same engine [1]. His survey work in this area includes "A Survey of Zero-Shot Generalisation in Deep Reinforcement Learning" (Journal of Artificial Intelligence Research, 2023), a widely cited review of how RL agents generalize to unseen situations [5].

### World models and self-improving systems

At DeepMind his research turned to generative world models and LLM-driven self-improvement. He shared two ICML 2024 Best Paper Awards: one for "Genie: Generative Interactive Environments," the foundation world model that generates controllable environments from video, and one for "Debating with More Persuasive LLMs Leads to More Truthful Answers," a study of using debate between language models to elicit truthful answers [7][12]. He was also a co-author of "Promptbreeder: Self-Referential Self-Improvement via Prompt Evolution" (ICML 2024), which evolves and improves the prompts that guide a language model [5].

## Recognition

Beyond the two ICML 2024 Best Paper Awards, Rocktäschel has received a Best Paper Award at the Conference on Automated Knowledge Base Construction in 2020 for "How Context Affects Language Models' Factual Predictions," a Best Paper Award at the EMNLP SocialNLP workshop in 2016 for "emoji2vec," and outstanding and highlighted area chair awards at ICLR in 2021 and 2022 [1]. His doctoral funding came from a Microsoft Research PhD Scholarship and a Google PhD Fellowship, and he was elected a Junior Research Fellow at Jesus College, Oxford [1].

## References

1. [Tim Rocktäschel, Curriculum Vitae (rockt.ai)](https://rockt.ai/rockt-cv.pdf)
2. [Tim Rocktäschel (personal page, rockt.ai)](https://rockt.ai/)
3. [AI startup Recursive emerges from stealth with $650 million to build self-improving AI | The Decoder](https://the-decoder.com/ai-startup-recursive-emerges-from-stealth-with-650-million-to-build-self-improving-ai/)
4. [Recursive Superintelligence emerges from stealth with $650M raise | Tech.eu](https://tech.eu/2026/05/13/recursive-superintelligence-emerges-from-stealth-with-650m-raise/)
5. [Tim Rocktäschel | Google Scholar](https://scholar.google.com/citations?user=mWBY8aIAAAAJ&hl=en)
6. [The NetHack Learning Environment | NeurIPS 2020](https://proceedings.neurips.cc/paper/2020/hash/569ff987c643b4bedf504efda8f786c2-Abstract.html)
7. [Genie: Generative Interactive Environments | ICML 2024](https://icml.cc/virtual/2024/poster/33646)
8. [The Inaugural Lecture of Professor Tim Rocktäschel | UCL Faculty of Engineering](https://www.ucl.ac.uk/engineering/computer-science/news-and-events/inaugural-lecture-series/inaugural-lecture-professor-tim-rocktaschel)
9. [What happens when AI starts building itself? | TechCrunch](https://techcrunch.com/2026/05/14/what-happens-when-ai-starts-building-itself/)
10. [Reasoning about Entailment with Neural Attention | arXiv](https://arxiv.org/abs/1509.06664)
11. [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks | arXiv](https://arxiv.org/abs/2005.11401)
12. [Congratulations to the ICML 2024 award winners | AIhub](https://aihub.org/2024/07/25/congratulations-to-the-icml2024-award-winners/)
13. [RECURSIVE SUPERINTELLIGENCE LTD. (company number 16937077), officers | Companies House](https://find-and-update.company-information.service.gov.uk/company/16937077/officers)