ChemSpot: a hybrid system for chemical named entity recognition. - Tim Rocktäschel; Michael Weidlich; Ulf Leser (Bioinform., 2012)

WBI-NER: The impact of domain-specific features on the performance of identifying and classifying mentions of drugs. - Tim Rocktäschel; Torsten Huber; Michael Weidlich; Ulf Leser (SemEval@NAACL-HLT, 2013)

WBI-DDI: Drug-Drug Interaction Extraction using Majority Voting. - Philippe Thomas 002; Mariana L. Neves; Tim Rocktäschel; Ulf Leser (SemEval@NAACL-HLT, 2013)

WOLFE: Strength Reduction and Approximate Programming for Probabilistic Programming. - Sebastian Riedel 001; Sameer Singh 001; Vivek Srikumar; Tim Rocktäschel; Larysa Visengeriyeva; Jan Noessner (StarAI@AAAI, 2014)

Towards Extracting Faithful and Descriptive Representations of Latent Variable Models. - Vicente Iván Sánchez Carmona; Tim Rocktäschel; Sebastian Riedel 001; Sameer Singh 001 (AAAI Spring Symposia, 2015)

Matrix and Tensor Factorization Methods for Natural Language Processing. - Guillaume Bouchard; Jason Naradowsky; Sebastian Riedel 001; Tim Rocktäschel; Andreas Vlachos 001 (ACL (Tutorial Abstracts), 2015)

Towards Combined Matrix and Tensor Factorization for Universal Schema Relation Extraction. - Sameer Singh 001; Tim Rocktäschel; Sebastian Riedel 001 (VS@HLT-NAACL, 2015)

Injecting Logical Background Knowledge into Embeddings for Relation Extraction. - Tim Rocktäschel; Sameer Singh 001; Sebastian Riedel 001 (HLT-NAACL, 2015)

WOLFE: An NLP-friendly Declarative Machine Learning Stack. - Sameer Singh 001; Tim Rocktäschel; Luke Hewitt; Jason Naradowsky; Sebastian Riedel 001 (HLT-NAACL, 2015)

Towards Two-Way Interaction with Reading Machines. - Sebastian Riedel 001; Sameer Singh 001; Guillaume Bouchard; Tim Rocktäschel; Vicente Iván Sánchez Carmona (SLSP, 2015)

The CHEMDNER corpus of chemicals and drugs and its annotation principles. - Martin Krallinger; Obdulia Rabal; Florian Leitner; Miguel Vazquez; David Salgado; Zhiyong Lu; Robert Leaman; Yanan Lu; Donghong Ji; Daniel M. Lowe; Roger A. Sayle; Riza Theresa Batista-Navarro; et al. (J. Cheminformatics, 2015)

emoji2vec: Learning Emoji Representations from their Description. - Ben Eisner; Tim Rocktäschel; Isabelle Augenstein; Matko Bosnjak; Sebastian Riedel 001 (SocialNLP@EMNLP, 2016)

Regularizing Relation Representations by First-order Implications. - Thomas Demeester; Tim Rocktäschel; Sebastian Riedel 001 (AKBC@NAACL-HLT, 2016)

Learning Knowledge Base Inference with Neural Theorem Provers. - Tim Rocktäschel; Sebastian Riedel 001 (AKBC@NAACL-HLT, 2016)

Stance Detection with Bidirectional Conditional Encoding. - Isabelle Augenstein; Tim Rocktäschel; Andreas Vlachos 001; Kalina Bontcheva (EMNLP, 2016)

Lifted Rule Injection for Relation Embeddings. - Thomas Demeester; Tim Rocktäschel; Sebastian Riedel 001 (EMNLP, 2016)

SETH detects and normalizes genetic variants in text. - Philippe Thomas 002; Tim Rocktäschel; Jörg Hakenberg; Yvonne Lichtblau; Ulf Leser (Bioinform., 2016)

Learning Python Code Suggestion with a Sparse Pointer Network. - Avishkar Bhoopchand; Tim Rocktäschel; Earl T. Barr; Sebastian Riedel 001 (CoRR, 2016)

Generating Natural Language Inference Chains. - Vladyslav Kolesnyk; Tim Rocktäschel; Sebastian Riedel 001 (CoRR, 2016)

Programming with a Differentiable Forth Interpreter. - Sebastian Riedel 001; Matko Bosnjak; Tim Rocktäschel (CoRR, 2016)

Reasoning about Entailment with Neural Attention. - Tim Rocktäschel; Edward Grefenstette; Karl Moritz Hermann; Tomás Kociský; Phil Blunsom (ICLR (Poster), 2016)

MuFuRU: The Multi-Function Recurrent Unit. - Dirk Weissenborn; Tim Rocktäschel (CoRR, 2016)

Frustratingly Short Attention Spans in Neural Language Modeling. - Michal Daniluk; Tim Rocktäschel; Johannes Welbl; Sebastian Riedel 001 (ICLR (Poster), 2017)

Programming with a Differentiable Forth Interpreter. - Matko Bosnjak; Tim Rocktäschel; Jason Naradowsky; Sebastian Riedel 001 (ICML, 2017)

End-to-end Differentiable Proving. - Tim Rocktäschel; Sebastian Riedel 001 (NIPS, 2017)

Adversarial Sets for Regularising Neural Link Predictors. - Pasquale Minervini; Thomas Demeester; Tim Rocktäschel; Sebastian Riedel 001 (UAI, 2017)

TreeQN and ATreeC: Differentiable Tree Planning for Deep Reinforcement Learning. - Gregory Farquhar; Tim Rocktäschel; Maximilian Igl; Shimon Whiteson (CoRR, 2017)

Combining Representation Learning with Logic for Language Processing. - Tim Rocktäschel (CoRR, 2017)

Jack the Reader - A Machine Reading Framework. - Dirk Weissenborn; Pasquale Minervini; Isabelle Augenstein; Johannes Welbl; Tim Rocktäschel; Matko Bosnjak; Jeff Mitchell 001; Thomas Demeester; Tim Dettmers; Pontus Stenetorp; Sebastian Riedel 001 (ACL (4), 2018)

Interpretation of Natural Language Rules in Conversational Machine Reading. - Marzieh Saeidi; Max Bartolo; Patrick Lewis 001; Sameer Singh 001; Tim Rocktäschel; Mike Sheldon; Guillaume Bouchard; Sebastian Riedel 001 (EMNLP, 2018)

TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning. - Gregory Farquhar; Tim Rocktäschel; Maximilian Igl; Shimon Whiteson (ICLR (Poster), 2018)

DiCE: The Infinitely Differentiable Monte Carlo Estimator. - Jakob N. Foerster; Gregory Farquhar; Maruan Al-Shedivat; Tim Rocktäschel; Eric P. Xing; Shimon Whiteson (ICML, 2018)

e-SNLI: Natural Language Inference with Natural Language Explanations. - Oana-Maria Camburu; Tim Rocktäschel; Thomas Lukasiewicz; Phil Blunsom (NeurIPS, 2018)

Towards Neural Theorem Proving at Scale. - Pasquale Minervini; Matko Bosnjak; Tim Rocktäschel; Sebastian Riedel 001 (CoRR, 2018)

Combining representation learning with logic for language processing. - Tim Rocktäschel (2018)

NLProlog: Reasoning with Weak Unification for Question Answering in Natural Language. - Leon Weber; Pasquale Minervini; Jannes Münchmeyer; Ulf Leser; Tim Rocktäschel (ACL (1), 2019)

Language Models as Knowledge Bases? - Fabio Petroni; Tim Rocktäschel; Sebastian Riedel 001; Patrick Lewis 001; Anton Bakhtin; Yuxiang Wu; Alexander H. Miller (EMNLP/IJCNLP (1), 2019)

Learning to Speak and Act in a Fantasy Text Adventure Game. - Jack Urbanek; Angela Fan; Siddharth Karamcheti; Saachi Jain; Samuel Humeau 001; Emily Dinan; Tim Rocktäschel; Douwe Kiela; Arthur Szlam; Jason Weston (EMNLP/IJCNLP (1), 2019)

Stable Opponent Shaping in Differentiable Games. - Alistair Letcher; Jakob N. Foerster; David Balduzzi; Tim Rocktäschel; Shimon Whiteson (ICLR (Poster), 2019)

A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs. - Jingkai Mao; Jakob N. Foerster; Tim Rocktäschel; Maruan Al-Shedivat; Gregory Farquhar; Shimon Whiteson (ICML, 2019)

A Survey of Reinforcement Learning Informed by Natural Language. - Jelena Luketina; Nantas Nardelli; Gregory Farquhar; Jakob N. Foerster; Jacob Andreas; Edward Grefenstette; Shimon Whiteson; Tim Rocktäschel (IJCAI, 2019)

Neural Variational Inference For Estimating Knowledge Graph Embedding Uncertainty. - Alexander I. Cowen-Rivers; Pasquale Minervini; Sebastian Riedel 001; Tim Rocktäschel; Jun Wang 012; Matko Bosnjak (NeSy@IJCAI, 2019)

Neural Variational Inference For Estimating Uncertainty in Knowledge Graph Embeddings. - Alexander I. Cowen-Rivers; Pasquale Minervini; Tim Rocktäschel; Matko Bosnjak; Sebastian Riedel 001; Jun Wang 012 (CoRR, 2019)

TorchBeast: A PyTorch Platform for Distributed RL. - Heinrich Küttler; Nantas Nardelli; Thibaut Lavril; Marco Selvatici; Viswanath Sivakumar; Tim Rocktäschel; Edward Grefenstette (CoRR, 2019)

MVFST-RL: An Asynchronous RL Framework for Congestion Control with Delayed Actions. - Viswanath Sivakumar; Tim Rocktäschel; Alexander H. Miller; Heinrich Küttler; Nantas Nardelli; Mike Rabbat; Joelle Pineau; Sebastian Riedel 001 (CoRR, 2019)

RTFM: Generalising to Novel Environment Dynamics via Reading. - Victor Zhong; Tim Rocktäschel; Edward Grefenstette (CoRR, 2019)

Generating Interactive Worlds with Text. - Angela Fan; Jack Urbanek; Pratik Ringshia; Emily Dinan; Emma Qian; Siddharth Karamcheti; Shrimai Prabhumoye; Douwe Kiela; Tim Rocktäschel; Arthur Szlam; Jason Weston (AAAI, 2020)

Differentiable Reasoning on Large Knowledge Bases and Natural Language. - Pasquale Minervini; Matko Bosnjak; Tim Rocktäschel; Sebastian Riedel 001; Edward Grefenstette (AAAI, 2020)

How Context Affects Language Models' Factual Predictions. - Fabio Petroni; Patrick Lewis 001; Aleksandra Piktus; Tim Rocktäschel; Yuxiang Wu; Alexander H. Miller; Sebastian Riedel 001 (AKBC, 2020)

How Decoding Strategies Affect the Verifiability of Generated Text. - Luca Massarelli; Fabio Petroni; Aleksandra Piktus; Myle Ott; Tim Rocktäschel; Vassilis Plachouras; Fabrizio Silvestri; Sebastian Riedel 001 (EMNLP (Findings), 2020)

Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training. - Joe Stacey; Pasquale Minervini; Haim Dubossarsky; Sebastian Riedel 001; Tim Rocktäschel (EMNLP (1), 2020)

RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments. - Roberta Raileanu; Tim Rocktäschel (ICLR, 2020)

RTFM: Generalising to New Environment Dynamics via Reading. - Victor Zhong; Tim Rocktäschel; Edward Grefenstette (ICLR, 2020)

Learning Reasoning Strategies in End-to-End Differentiable Proving. - Pasquale Minervini; Sebastian Riedel 001; Pontus Stenetorp; Edward Grefenstette; Tim Rocktäschel (ICML, 2020)

The NetHack Learning Environment. - Heinrich Küttler; Nantas Nardelli; Alexander H. Miller; Roberta Raileanu; Marco Selvatici; Edward Grefenstette; Tim Rocktäschel (NeurIPS, 2020)

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. - Patrick Lewis 001; Ethan Perez; Aleksandra Piktus; Fabio Petroni; Vladimir Karpukhin; Naman Goyal 001; Heinrich Küttler; Mike Lewis; Wen-tau Yih; Tim Rocktäschel; Sebastian Riedel 001; Douwe Kiela (NeurIPS, 2020)

HUNER: improving biomedical NER with pretraining. - Leon Weber; Jannes Münchmeyer; Tim Rocktäschel; Maryam Habibi; Ulf Leser (Bioinform., 2020)

There is Strength in Numbers: Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training. - Joe Stacey; Pasquale Minervini; Haim Dubossarsky; Sebastian Riedel 001; Tim Rocktäschel (CoRR, 2020)

WordCraft: An Environment for Benchmarking Commonsense Agents. - Minqi Jiang; Jelena Luketina; Nantas Nardelli; Pasquale Minervini; Philip H. S. Torr; Shimon Whiteson; Tim Rocktäschel (CoRR, 2020)

KILT: a Benchmark for Knowledge Intensive Language Tasks. - Fabio Petroni; Aleksandra Piktus; Angela Fan; Patrick Lewis 001; Majid Yazdani; Nicola De Cao; James Thorne; Yacine Jernite; Vassilis Plachouras; Tim Rocktäschel; Sebastian Riedel 001 (CoRR, 2020)

Differentiable Reasoning on Large Knowledge Bases and Natural Language. - Pasquale Minervini; Matko Bosnjak; Tim Rocktäschel; Sebastian Riedel 001; Edward Grefenstette (Knowledge Graphs for eXplainable Artificial Intelligence, 2020)

Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning. - Zhengyao Jiang; Pasquale Minervini; Minqi Jiang; Tim Rocktäschel (AAMAS, 2021)

Learning with AMIGo: Adversarially Motivated Intrinsic Goals. - Andres Campero; Roberta Raileanu; Heinrich Küttler; Joshua B. Tenenbaum; Tim Rocktäschel; Edward Grefenstette (ICLR, 2021)

My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control. - Vitaly Kurin; Maximilian Igl; Tim Rocktäschel; Wendelin Boehmer; Shimon Whiteson (ICLR, 2021)

Prioritized Level Replay. - Minqi Jiang; Edward Grefenstette; Tim Rocktäschel (ICML, 2021)

How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds. - Prithviraj Ammanabrolu; Jack Urbanek; Margaret Li; Arthur Szlam; Tim Rocktäschel; Jason Weston (NAACL-HLT, 2021)

KILT: a Benchmark for Knowledge Intensive Language Tasks. - Fabio Petroni; Aleksandra Piktus; Angela Fan; Patrick Lewis 001; Majid Yazdani; Nicola De Cao; James Thorne; Yacine Jernite; Vladimir Karpukhin; Jean Maillard; Vassilis Plachouras; Tim Rocktäschel; et al. (NAACL-HLT, 2021)

Insights From the NeurIPS 2021 NetHack Challenge. - Eric Hambro; Sharada P. Mohanty; Dmitrii Babaev; Minwoo Byeon; Dipam Chakraborty; Edward Grefenstette; Minqi Jiang; DaeJin Jo; Anssi Kanervisto; Jongmin Kim 006; Sungwoong Kim; Robert Kirk; et al. (NeurIPS (Competition and Demos), 2021)

Replay-Guided Adversarial Environment Design. - Minqi Jiang; Michael Dennis 001; Jack Parker-Holder; Jakob N. Foerster; Edward Grefenstette; Tim Rocktäschel (NeurIPS, 2021)

MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research. - Mikayel Samvelyan; Robert Kirk; Vitaly Kurin; Jack Parker-Holder; Minqi Jiang; Eric Hambro; Fabio Petroni; Heinrich Küttler; Edward Grefenstette; Tim Rocktäschel (NeurIPS Datasets and Benchmarks, 2021)

Don't Sweep your Learning Rate under the Rug: A Closer Look at Cross-modal Transfer of Pretrained Transformers. - Danielle Rothermel; Margaret Li; Tim Rocktäschel; Jakob N. Foerster (CoRR, 2021)

A Survey of Generalisation in Deep Reinforcement Learning. - Robert Kirk; Amy Zhang 001; Edward Grefenstette; Tim Rocktäschel (CoRR, 2021)

Learning Reasoning Strategies in End-to-End Differentiable Proving. - Pasquale Minervini; Sebastian Riedel 001; Pontus Stenetorp; Edward Grefenstette; Tim Rocktäschel (Neuro-Symbolic Artificial Intelligence, 2021)

Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning. - Michael T. Matthews; Mikayel Samvelyan; Jack Parker-Holder; Edward Grefenstette; Tim Rocktäschel (CoLLAs, 2022)

Evolving Curricula with Regret-Based Environment Design. - Jack Parker-Holder; Minqi Jiang; Michael Dennis 001; Mikayel Samvelyan; Jakob N. Foerster; Edward Grefenstette; Tim Rocktäschel (ICML, 2022)

GriddlyJS: A Web IDE for Reinforcement Learning. - Christopher Bamford 001; Minqi Jiang; Mikayel Samvelyan; Tim Rocktäschel (NeurIPS, 2022)

Dungeons and Data: A Large-Scale NetHack Dataset. - Eric Hambro; Roberta Raileanu; Danielle Rothermel; Vegard Mella; Tim Rocktäschel; Heinrich Küttler; Naila Murray (NeurIPS, 2022)

Exploration via Elliptical Episodic Bonuses. - Mikael Henaff; Roberta Raileanu; Minqi Jiang; Tim Rocktäschel (NeurIPS, 2022)

Grounding Aleatoric Uncertainty for Unsupervised Environment Design. - Minqi Jiang; Michael Dennis 001; Jack Parker-Holder; Andrei Lupu; Heinrich Küttler; Edward Grefenstette; Tim Rocktäschel; Jakob N. Foerster (NeurIPS, 2022)

Improving Intrinsic Exploration with Language Abstractions. - Jesse Mu; Victor Zhong; Roberta Raileanu; Minqi Jiang; Noah D. Goodman; Tim Rocktäschel; Edward Grefenstette (NeurIPS, 2022)

Learning General World Models in a Handful of Reward-Free Deployments. - Yingchen Xu; Jack Parker-Holder; Aldo Pacchiano; Philip J. Ball; Oleh Rybkin; Stephen Roberts 001; Tim Rocktäschel; Edward Grefenstette (NeurIPS, 2022)

Improving Policy Learning via Language Dynamics Distillation. - Victor Zhong; Jesse Mu; Luke Zettlemoyer; Edward Grefenstette; Tim Rocktäschel (NeurIPS, 2022)

Generalization in Cooperative Multi-Agent Systems. - Anuj Mahajan; Mikayel Samvelyan; Tarun Gupta 002; Benjamin Ellis; Mingfei Sun 001; Tim Rocktäschel; Shimon Whiteson (CoRR, 2022)

Graph Backup: Data Efficient Backup Exploiting Markovian Transitions. - Zhengyao Jiang; Tianjun Zhang; Robert Kirk; Tim Rocktäschel; Edward Grefenstette (CoRR, 2022)

Large language models are not zero-shot communicators. - Laura Ruis; Akbir Khan; Stella Biderman; Sara Hooker; Tim Rocktäschel; Edward Grefenstette (CoRR, 2022)

General Intelligence Requires Rethinking Exploration. - Minqi Jiang; Tim Rocktäschel; Edward Grefenstette (CoRR, 2022)

Stabilizing Unsupervised Environment Design with a Learned Adversary. - Ishita Mediratta; Minqi Jiang; Jack Parker-Holder; Michael Dennis 001; Eugene Vinitsky; Tim Rocktäschel (CoLLAs, 2023)

Efficient Planning in a Compact Latent Action Space. - Zhengyao Jiang; Tianjun Zhang; Michael Janner; Yueying Li; Tim Rocktäschel; Edward Grefenstette; Yuandong Tian (ICLR, 2023)

MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning. - Mikayel Samvelyan; Akbir Khan; Michael Dennis 001; Minqi Jiang; Jack Parker-Holder; Jakob Nicolaus Foerster; Roberta Raileanu; Tim Rocktäschel (ICLR, 2023)

Human-Timescale Adaptation in an Open-Ended Task Space. - Jakob Bauer; Kate Baumli; Feryal M. P. Behbahani; Avishkar Bhoopchand; Nathalie Bradley-Schmieg; Michael Chang; Natalie Clay; Adrian Collister; Vibhavari Dasagi; Lucy Gonzalez; Karol Gregor; Edward Hughes 001; et al. (ICML, 2023)

The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs. - Laura Ruis; Akbir Khan; Stella Biderman; Sara Hooker; Tim Rocktäschel; Edward Grefenstette (NeurIPS, 2023)

Human-Timescale Adaptation in an Open-Ended Task Space. - Adaptive Agent Team; Jakob Bauer; Kate Baumli; Satinder Baveja; Feryal M. P. Behbahani; Avishkar Bhoopchand; Nathalie Bradley-Schmieg; Michael Chang; Natalie Clay; Adrian Collister; Vibhavari Dasagi; Lucy Gonzalez; et al. (CoRR, 2023)

Mix-ME: Quality-Diversity for Multi-Agent Learning. - Garðar Ingvarsson; Mikayel Samvelyan; Bryan Lim; Manon Flageat; Antoine Cully; Tim Rocktäschel (CoRR, 2023)

JaxMARL: Multi-Agent RL Environments in JAX. - Alexander Rutherford; Benjamin Ellis; Matteo Gallici; Jonathan Cook 004; Andrei Lupu; Garðar Ingvarsson; Timon Willi; Akbir Khan; Christian Schröder de Witt; Alexandra Souly; Saptarashmi Bandyopadhyay; Mikayel Samvelyan; et al. (CoRR, 2023)

minimax: Efficient Baselines for Autocurricula in JAX. - Minqi Jiang; Michael Dennis 001; Edward Grefenstette; Tim Rocktäschel (CoRR, 2023)

Vision-Language Models as a Source of Rewards. - Kate Baumli; Satinder Baveja; Feryal M. P. Behbahani; Harris Chan; Gheorghe Comanici; Sebastian Flennerhag; Maxime Gazeau; Kristian Holsheimer; Dan Horgan; Michael Laskin; Clare Lyle; Hussain Masoom; et al. (CoRR, 2023)

Leading the Pack: N-player Opponent Shaping. - Alexandra Souly; Timon Willi; Akbir Khan; Robert Kirk; Chris Lu 001; Edward Grefenstette; Tim Rocktäschel (CoRR, 2023)

A Survey of Zero-shot Generalisation in Deep Reinforcement Learning. - Robert Kirk; Amy Zhang 001; Edward Grefenstette; Tim Rocktäschel (J. Artif. Intell. Res., 2023)

Scaling Opponent Shaping to High Dimensional Games. - Akbir Khan; Timon Willi; Newton Kwan; Andrea Tacchetti; Chris Lu 001; Edward Grefenstette; Tim Rocktäschel; Jakob N. Foerster (AAMAS, 2024)

JaxMARL: Multi-Agent RL Environments and Algorithms in JAX. - Alexander Rutherford; Benjamin Ellis; Matteo Gallici; Jonathan Cook 004; Andrei Lupu; Garðar Ingvarsson; Timon Willi; Akbir Khan; Christian Schröder de Witt; Alexandra Souly; Saptarashmi Bandyopadhyay; Mikayel Samvelyan; et al. (AAMAS, 2024)

Multi-Agent Diagnostics for Robustness via Illuminated Diversity. - Mikayel Samvelyan; Davide Paglieri; Minqi Jiang; Jack Parker-Holder; Tim Rocktäschel (AAMAS, 2024)

Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks. - Samyak Jain; Robert Kirk; Ekdeep Singh Lubana; Robert P. Dick; Hidenori Tanaka; Tim Rocktäschel; Edward Grefenstette; David Scott Krueger (ICLR, 2024)

H-GAP: Humanoid Control with a Generalist Planner. - Zhengyao Jiang; Yingchen Xu; Nolan Wagener; Yicheng Luo; Michael Janner; Edward Grefenstette; Tim Rocktäschel; Yuandong Tian (ICLR, 2024)

Position: Open-Endedness is Essential for Artificial Superhuman Intelligence. - Edward Hughes 001; Michael D. Dennis; Jack Parker-Holder; Feryal M. P. Behbahani; Aditi Mavalankar; Yuge Shi; Tom Schaul; Tim Rocktäschel (ICML, 2024)

Genie: Generative Interactive Environments. - Jake Bruce; Michael D. Dennis; Ashley Edwards; Jack Parker-Holder; Yuge Shi; Edward Hughes 001; Matthew Lai; Aditi Mavalankar; Richie Steigerwald; Chris Apps; Yusuf Aytar; Sarah Bechtle; et al. (ICML, 2024)

Promptbreeder: Self-Referential Self-Improvement via Prompt Evolution. - Chrisantha Fernando; Dylan Banarse; Henryk Michalewski; Simon Osindero; Tim Rocktäschel (ICML, 2024)

Debating with More Persuasive LLMs Leads to More Truthful Answers. - Akbir Khan; John Hughes; Dan Valentine; Laura Ruis; Kshitij Sachan; Ansh Radhakrishnan; Edward Grefenstette; Samuel R. Bowman; Tim Rocktäschel; Ethan Perez (ICML, 2024)

JaxMARL: Multi-Agent RL Environments and Algorithms in JAX. - Alexander Rutherford; Benjamin Ellis; Matteo Gallici; Jonathan Cook 004; Andrei Lupu; Garðar Ingvarsson; Timon Willi; Ravi Hammond; Akbir Khan; Christian Schröder de Witt; Alexandra Souly; Saptarashmi Bandyopadhyay; et al. (NeurIPS, 2024)

Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts. - Mikayel Samvelyan; Sharath Chandra Raparthy; Andrei Lupu; Eric Hambro; Aram H. Markosyan; Manish Bhatt; Yuning Mao; Minqi Jiang; Jack Parker-Holder; Jakob N. Foerster; Tim Rocktäschel; Roberta Raileanu (NeurIPS, 2024)

Outliers and Calibration Sets have Diminishing Effect on Quantization of Modern LLMs. - Davide Paglieri; Saurabh Dash; Tim Rocktäschel; Jack Parker-Holder (CoRR, 2024)

Open-Endedness is Essential for Artificial Superhuman Intelligence. - Edward Hughes 001; Michael Dennis 001; Jack Parker-Holder; Feryal M. P. Behbahani; Aditi Mavalankar; Yuge Shi; Tom Schaul; Tim Rocktäschel (CoRR, 2024)

Assessing the Zero-Shot Capabilities of LLMs for Action Evaluation in RL. - Eduardo Pignatelli; Johan Ferret; Tim Rocktäschel; Edward Grefenstette; Davide Paglieri; Samuel Coward; Laura Toni (CoRR, 2024)

TICKing All the Boxes: Generated Checklists Improve LLM Evaluation and Generation. - Jonathan Cook 004; Tim Rocktäschel; Jakob N. Foerster; Dennis Aumiller; Alex Wang (CoRR, 2024)

Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models. - Laura Ruis; Maximilian Mozes; Juhan Bae; Siddhartha Rao Kamalakara; Dwarak Talupuru; Acyr Locatelli; Robert Kirk; Tim Rocktäschel; Edward Grefenstette; Max Bartolo (CoRR, 2024)

BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games. - Davide Paglieri; Bartlomiej Cupial; Samuel Coward; Ulyana Piterbarg; Maciej Wolczyk; Akbir Khan; Eduardo Pignatelli; Lukasz Kucinski; Lerrel Pinto; Rob Fergus; Jakob Nicolaus Foerster; Jack Parker-Holder; et al. (ICLR, 2025)

Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models. - Laura Ruis; Maximilian Mozes; Juhan Bae; Siddhartha Rao Kamalakara; Dwaraknath Gnaneshwar; Acyr Locatelli; Robert Kirk; Tim Rocktäschel; Edward Grefenstette; Max Bartolo (ICLR, 2025)

Investigating Non-Transitivity in LLM-as-a-Judge. - Yi Xu; Laura Ruis; Tim Rocktäschel; Robert Kirk (CoRR, 2025)

D3PO: Preference-Based Alignment of Discrete Diffusion Models. - Umberto Borso; Davide Paglieri; Jude Wells; Tim Rocktäschel (CoRR, 2025)

LLM-First Search: Self-Guided Exploration of the Solution Space. - Nathan Herr; Tim Rocktäschel; Roberta Raileanu (CoRR, 2025)

Programming by Backprop: LLMs Acquire Reusable Algorithmic Abstractions During Code Training. - Jonathan Cook 004; Silvia Sapora; Arash Ahmadian; Akbir Khan; Tim Rocktäschel; Jakob N. Foerster; Laura Ruis (CoRR, 2025)