David Abel

@dabelcs

Scientist @GoogleDeepMind and Fellow @EdinburghUni | RL, philosophy, agency, foundations, alignment | office hours: tinyurl.com/dabel-mtg

Edinburgh, Scotland

david-abel.github.io

Joined January 2018

Pinned
David Abel
@dabelcs
Nov 4, 2025
Thrilled to share our new #NeurIPS2025 paper done at @GoogleDeepMind, Plasticity as the Mirror of Empowerment We prove every agent faces a trade-off between its capacity to adapt (plasticity) and its capacity to steer (empowerment) Paper: david-abel.github.io/plasticity.pdf 🧵🧵🧵👇
David Abel
@dabelcs
Dec 6, 2025
Amazing opportunity to work with @ermgrant
Erin Grant
@ermgrant
Dec 6, 2025
Thrilled to announce I'll start in 2026 as faculty in Psych & CS @UAlberta + @AmiiThinks Fellow!! 🥳 Recruiting students to develop theories of cognition in natural and artificial systems 🤖💭🧠. Find me at #NeurIPS2025 workshops (talk at @CogInterp & organising @DataOnBrainMind)
David Abel
@dabelcs
Dec 4, 2025
Interested in the theory of agents / definitions / RL under minimal assumptions? I'll be at poster #512 today (Thurs.) in the 4.30pm-7.30pm session. Come on by 🙂
David Abel
@dabelcs
Nov 4, 2025
Thrilled to share our new #NeurIPS2025 paper done at @GoogleDeepMind, Plasticity as the Mirror of Empowerment We prove every agent faces a trade-off between its capacity to adapt (plasticity) and its capacity to steer (empowerment) Paper: david-abel.github.io/plasticity.pdf 🧵🧵🧵👇
David Abel
@dabelcs
Dec 3, 2025
This is a very thoughtful paper They give a careful construction of an agent that is embedded in its environment: go talk to Alex!
Alex Lewandowski
@axlewandowski
Dec 3, 2025
At NeurIPS and interested in continual learning? Stop by our spotlight poster this Friday @ 11am (#508). Our work provides a computational approach to the big world hypothesis through embedded agents. Feel free to reach out if you want to meet up while in San Diego!
David Abel
@dabelcs
Jun 5, 2025
Excited about this new work led by @jonathanrichens, joint with @alexis_bellot_ and @tom4everitt Main result: Any agent that can solve a sufficiently rich set of goal-directed tasks must have learned a predictive model of the environment arxiv.org/abs/2506.01622
Jon Richens
@jonathanrichens
Jun 4, 2025
Are world models necessary to achieve human-level agents, or is there a model-free short-cut? Our new #ICML2025 paper tackles this question from first principles, and finds a surprising answer, agents _are_ world models… 🧵