
Scientist @GoogleDeepMind and Fellow @EdinburghUni | RL, philosophy, agency, foundations, alignment | office hours: https://xitter.cf/t.co/XrrHAXXHi5
Edinburgh, Scotland
Joined January 2018
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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/plastic…
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RLDM is returning in 2027!
📣 Save the date!
RLDM 2027, the Multi-disciplinary Conference on Reinforcement Learning and Decision Making, is coming to Paris, July 6-9, 2027 🎉
Stay tuned for submission deadlines and more updates on our website rldm.org 👀
David Abel retweeted
viva passed with minor corrections! Thanks to @MichaelD1729 and @ale_suglia for serving as my examiners and to my wonderful advisors @dabelcs and Chris Lucas for the incredible mentorship and support over these past years!
David Abel retweeted
Attending #ICML and interested in World Models?
PERSIST models the world within an explicit 3D latent representation. Come by to discover the unique capabilities this unlocks!
📍 Poster 2715 • Thursday • 2:30pm
#ICML2026 #WorldModels #GenerativeAI #3DGeneration
David Abel retweeted
This week's #PaperILike is "General agents contain world models" (Richens et al., ICML 2025).
Brought to my attn by @dabelcs 's beautiful philosophy-laden talk at ICAPS. The explicit construction of a transition model from a policy is cool (Alg 1).
PDF: arxiv.org/abs/2506.01622
David Abel retweeted
Model-free agents learn to maximise reward without modelling the environment. Right?
In recent work, we challenge this narrative by proving that agents, trained on a sufficiently rich set of goals, encode a unique and accurate world model in their value functions.
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David Abel retweeted
We are starting a new, nonprofit alignment organization, ⊢ Sequent Research, bringing together researchers previously on UK AISI’s Alignment Team, Timaeus, and elsewhere to research how to align superintelligence. We are hiring! 🧵
David Abel retweeted
Just published in @PNASNews, we resolve a 50-year-old riddle from Richard Feynman's handwritten notes, prove and generalize it, and run a large-scale human study to reveal near-optimal heuristics in sequential decision problems:
doi.org/10.1073/pnas.2509612…
David Abel retweeted
"Imperfect World Models are Exploitable"
World models can look accurate, but still rank policies incorrectly, saying policy A is better than policy B when the real environment says the reverse.
This paper formalizes that failure as model exploitation and proves it is basically unavoidable for any nontrivial, nonequivalent world model on broad policy sets.
It also connects this to reward hacking and derives a safe horizon showing how model error compounds with planning depth.
David Abel retweeted
Reminder that the deadline for the Finding the Frame workshop @RL_Conference is coming up on May 22 AoE ⏰
We're excited to read your submissions reflecting on the philosophy, practice, and formalisms of reinforcement learning!
🔗 More details: sites.google.com/view/findin…
David Abel retweeted
Finding the Frame will be back at @RL_Conference 2026 with a fantastic speaker lineup! We welcome submissions that reflect on the philosophy, practice, and formalisms of reinforcement learning.
📅Submission deadline: May 22, 2026 (AoE)
More details: sites.google.com/view/findin…
David Abel retweeted
Excited to be at (my first) #ICLR2026 this week to present work with Tom Griffiths (@cocosci_lab) on efficient exploration for LLM agents 🧵
David Abel retweeted
Standard RL assumes a stable world. The real world may not.
♾ Introducing the Continual RL Workshop @RL_Conference 2026, Montreal, Canada.
🤖 Agents should never stop learning!
🖼️ Site: sites.google.com/view/contin…
📄 Submit: sites.google.com/view/contin…
David Abel retweeted
Really excited to present our recent work at #ICLR2026 this week!
We discover highly coordinated joint behaviours and integrate them into the skill sets of MARL agents, accelerating the search for effective joint strategies in downstream tasks.🧵
Paper: raulsteleac.github.io/iaro
David Abel retweeted
New preprint of a paper with Eunice Yiu to appear in Philosophical Transactions A, Special issue: World models, 2026. The theoretical link between empowerment in RL and Bayesian causal models with cool new data. arxiv.org/abs/2512.08230
David Abel retweeted
We're thrilled to announce Finding the Frame is back for the third time @RL_Conference 2026!🎉 Call for papers coming soon 🚀
David Abel retweeted
We have the keynote speakers for RLC2026 now: Thrilled to welcome @contactrika, @ravi_iitm, @SheilaMcIlraith, @marcgbellemare, and @danijarh!
Details: rl-conference.cc/index.html
The RL community is coming together this August in Montréal, Québec, Canada. Hope you make it!
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David Abel retweeted
The "decoupling of information and energy" is a major point of divergence between biological and artificial computers.
Brains are efficient, modern AI isn't. And energy consumption is the biggest bottleneck in scaling AI (you can't hallucinate electrons into existence).
To address this we need an "energy-aware theory of computation." And this new preprint is an attempt to address this.
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David Abel retweeted
Newton gave force, inertia, and motion precise mathematical definitions. This unlocked centuries of progress in mechanics.
Turing did it for "computation," and Shannon for "information."
Today, "agency" still feels a bit like "computation" before Turing: everybody uses the word, but there is no widely accepted precise definition.
@dabelcs argue that RL needs to take this problem more seriously.
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David Abel retweeted
PERSIST is a world model that ditches pixel-based histories for a 3D world state. Instead of searching through an ever-growing sequence of past pixel observations, PERSIST retrieves spatial information from a dynamically evolving 3D representation.
This change improves the spatial memory, 3D consistency, and long-horizon stability of the model, enabling interactive experiences within coherent and evolving 3D worlds.
#MachineLearning #WorldModels #GenerativeAI #3DComputerVision #ComputerVision #Genie3 #AI


























