Tim Rocktäschel
Professor of Artificial Intelligence
Researches open-endedness, reinforcement learning and self-improving AI agents.
Tim Rocktäschel's lab at UCL works on reinforcement learning. 41 papers since 2023, 21 at top venues, mostly ICLR and ICML.
At a glance
- papers, 2023–2026
- 11/ year
- at T1 venues
- 51%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 7 papers2026 · T1 3 · collecting
- 6 papers2025 · T1 4
- 15 papers2024 · T1 10
- 13 papers2023 · T1 4
T1 venuesOtherVenues: ICLR, ICML, NeurIPS, AAMAS, ACL
Research focuspaper titles that use each topic's words
- Reinforcement learning6 papers
- agents2 papers
- reinforcement2 papers
- researches0 papers
- computer science0 papers
Paper list
From the lab homepage.
20267 papers
- NeurIPS (T1)Benchmarking Open-Ended Multi-Agent Coordination in Language Agents (opens in a new tab)
- ACL (T1)Check Your Work: Structured Checklist Feedback for Improving Large Language Models (opens in a new tab)
- LLADèjàQ: Open-ended Evolution of Diverse, Learnable and Verifiable Problems (opens in a new tab)
- https://www.recursive.com/articles/ -steps-toward-automated-ai-researchFirst Steps Toward Automated AI Research (opens in a new tab)
- Meta-AgentsLearning When to Plan: Efficiently Allocating Test-Time Compute for LLM Agents (opens in a new tab)
- ICLR (T1)Programming by Backprop: An Instruction is Worth 100 Examples When Finetuning LLMs (opens in a new tab)
- Verify-AgentsStress-Testing the Truth Advantage in Language Model Debates (opens in a new tab)
20256 papers
- ICLR (T1)BALROG: Benchmarking Agentic LLM and VLM Reasoning on Games (opens in a new tab)
- Bi-AlignD3PO: Preference-Based Alignment of Discrete Diffusion Models (opens in a new tab)
- https://deepmind.google/discover/blog/genie-3-a-new-frontier-for-world-models/Genie 3: A New Frontier for World Models (opens in a new tab)
- NeurIPS (T1)Imagined Autocurricula (opens in a new tab)
- ICML (T1)Investigating Non-Transitivity in LLM-as-a-Judge (opens in a new tab)
- ICLR (T1)Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models (opens in a new tab)
202415 papers
- —Artificial Intelligence: 10 Things You Should Know (opens in a new tab)
- AutoRLAssessing the Zero-Shot Capabilities of LLMs for Action Evaluation in RL (opens in a new tab)
- ICML (T1)Debating with More Persuasive LLMs Leads to More Truthful Answers (opens in a new tab)
- https://deepmind.google/discover/blog/genie-2-a-large-scale-foundation-world-model/Genie 2: A Large-Scale Foundation World Model (opens in a new tab)
- ICML (T1)Genie: Generative Interactive Environments (opens in a new tab)
- ICLR (T1)H-GAP: Humanoid Control with a Generalist Planner (opens in a new tab)
- NeurIPS (T1)JaxMARL: Multi-Agent RL Environments and Algorithms in JAX (opens in a new tab)
- ICLR (T1)Mechanistically Analyzing the Effects of Fine-tuning on Procedurally Defined Tasks (opens in a new tab)
- AAMAS (T1)Multi-Agent Diagnostics for Robustness via Illuminated Diversity (opens in a new tab)
- LanGameOn Reward Functions For Self-Improving General-Purpose Reasoning (opens in a new tab)
- ICML (T1)Open-Endedness is Essential for Artificial Superhuman Intelligence (opens in a new tab)
- ES-FoMo-IIOutliers and Calibration Sets have Diminishing Effect on Quantization of Modern LLMs (opens in a new tab)
202313 papers
- Journal of Artificial Intelligence ResearchA Survey of Zero-Shot Generalisation in Deep Reinforcement Learning (opens in a new tab)
- https://github.com/Farama-Foundation/chatarenaChatArena: Multi-agent Language Game Environments for Large Language Models (opens in a new tab)
- ToMDo LLMs Selectively Encode the Goal of an Agent's Reach? (opens in a new tab)
- ICLR (T1)Efficient Planning in a Compact Latent Action Space (opens in a new tab)
- Royal Society Open ScienceGeneral Intelligence Requires Rethinking Exploration (opens in a new tab)
- ICML (T1)Human-Timescale Adaptation in an Open-Ended Task Space (opens in a new tab)
- MASECLeading the Pack: N-player Opponent Shaping (opens in a new tab)
- ICLR (T1)MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning (opens in a new tab)
- ALOEminimax: Efficient Baselines for Autocurricula in JAX (opens in a new tab)
- ALOEMix-ME: Quality-Diversity for Multi-Agent Learning (opens in a new tab)
- CoLLAsStabilizing Unsupervised Environment Design with a Learned Adversary (opens in a new tab)
- NeurIPS (T1)The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to UCLFall 2027
Not announced yet
Computer Science MPhil/PhD
- Round
- Main
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
Similar labsnearest by research summary
- USMIT
Gabriele Farina
- Learning theory & optimization
- GBUCL
Benjamin Guedj
- Learning theory & optimization
- CAToronto
David Duvenaud
- Probabilistic & causal inference
- CNPeking
Tiejun Huang
- Computational imaging & photography
- SGNUS
Jonathan Scarlett
- Learning theory & optimization
- CNSJTU
Yue Ding
- Vision-language & multimodal
Your notes
Save this lab to keep notes.
Data
Some numbers could not be verified:
- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Not collected
- Alumni
- Not collected
Tiers are venue classifications, not lab rankings.