Pierre-Luc Bacon

Razvan Pascanu

Aristide Baratin

2025-02-13

TMLR (accepted)

Mol-MoE: Training Preference-Guided Routers for Molecule Generation

Diego Calanzone

Pierluca D'Oro

Recent advances in language models have enabled framing molecule generation as sequence modeling. However, existing approaches often rely on… (see more) single-objective reinforcement learning, limiting their applicability to real-world drug design, where multiple competing properties must be optimized. Traditional multi-objective reinforcement learning (MORL) methods require costly retraining for each new objective combination, making rapid exploration of trade-offs impractical. To overcome these limitations, we introduce Mol-MoE, a mixture-of-experts (MoE) architecture that enables efficient test-time steering of molecule generation without retraining. Central to our approach is a preference-based router training objective that incentivizes the router to combine experts in a way that aligns with user-specified trade-offs. This provides improved flexibility in exploring the chemical property space at test time, facilitating rapid trade-off exploration. Benchmarking against state-of-the-art methods, we show that Mol-MoE achieves superior sample quality and steerability.

2025-02-08

ArXiv (preprint)

MaestroMotif: Skill Design from Artificial Intelligence Feedback

Martin Klissarov

Mikael Henaff

Roberta Raileanu

Shagun Sodhani

Pascal Vincent

Amy Zhang

Marlos C. Machado

Pierluca D'Oro

Describing skills in natural language has the potential to provide an accessible way to inject human knowledge about decision-making into an… (see more) AI system. We present MaestroMotif, a method for AI-assisted skill design, which yields high-performing and adaptable agents. MaestroMotif leverages the capabilities of Large Language Models (LLMs) to effectively create and reuse skills. It first uses an LLM's feedback to automatically design rewards corresponding to each skill, starting from their natural language description. Then, it employs an LLM's code generation abilities, together with reinforcement learning, for training the skills and combining them to implement complex behaviors specified in language. We evaluate MaestroMotif using a suite of complex tasks in the NetHack Learning Environment (NLE), demonstrating that it surpasses existing approaches in both performance and usability.

2025-01-22

ICLR.cc/2025/Conference (oral)

MaestroMotif: Skill Design from Artificial Intelligence Feedback

Martin Klissarov

Mikael Henaff

Roberta Raileanu

Shagun Sodhani

Pascal Vincent

Amy Zhang

Marlos C. Machado

Pierluca D'Oro

Describing skills in natural language has the potential to provide an accessible way to inject human knowledge about decision-making into an… (see more) AI system. We present MaestroMotif, a method for AI-assisted skill design, which yields high-performing and adaptable agents. MaestroMotif leverages the capabilities of Large Language Models (LLMs) to effectively create and reuse skills. It first uses an LLM's feedback to automatically design rewards corresponding to each skill, starting from their natural language description. Then, it employs an LLM's code generation abilities, together with reinforcement learning, for training the skills and combining them to implement complex behaviors specified in language. We evaluate MaestroMotif using a suite of complex tasks in the NetHack Learning Environment (NLE), demonstrating that it surpasses existing approaches in both performance and usability.

2024-12-11

ArXiv (preprint)

MaestroMotif: Skill Design from Artificial Intelligence Feedback

Martin Klissarov

Mikael Henaff

Roberta Raileanu

Shagun Sodhani

Pascal Vincent

Amy Zhang

Marlos C. Machado

Pierluca D'Oro

Describing skills in natural language has the potential to provide an accessible way to inject human knowledge about decision-making into an… (see more) AI system. We present MaestroMotif, a method for AI-assisted skill design, which yields high-performing and adaptable agents. MaestroMotif leverages the capabilities of Large Language Models (LLMs) to effectively create and reuse skills. It first uses an LLM's feedback to automatically design rewards corresponding to each skill, starting from their natural language description. Then, it employs an LLM's code generation abilities, together with reinforcement learning, for training the skills and combining them to implement complex behaviors specified in language. We evaluate MaestroMotif using a suite of complex tasks in the NetHack Learning Environment (NLE), demonstrating that it surpasses existing approaches in both performance and usability.

2024-12-11

ArXiv (preprint)

Neural differential equations for temperature control in buildings under demand response programs

Vincent Taboga

Clement Gehring

Mathieu Le Cam

Hanane Dagdougui

2024-08-01

Applied Energy (published)

Neural differential equations for temperature control in buildings under demand response programs

Vincent Taboga

Clement Gehring

Mathieu Le Cam

Hanane Dagdougui

2024-08-01

Applied Energy (published)

Effects of Scale on Language Model Robustness

Nikolaus H. R. Howe

Ian R. McKenzie

Oskar John Hollinsworth

Michał Zając

Tom Tseng

Aaron David Tucker

Adam Gleave

Language models exhibit scaling laws, whereby increasing model and dataset size yields predictable decreases in negative log likelihood, unl… (see more)ocking a dazzling array of capabilities. This phenomenon spurs many companies to train ever larger models in pursuit of ever improved performance. Yet, these models are vulnerable to adversarial inputs such as ``jailbreaks'' and prompt injections that induce models to perform undesired behaviors, posing a growing risk as models become more capable. Prior work indicates that computer vision models become more robust with model and data scaling, raising the question: does language model robustness also improve with scale? We study this question empirically in the classification setting, finding that without explicit defense training, larger models tend to be modestly more robust on most tasks, though the effect is not reliable. Even with the advantage conferred by scale, undefended models remain easy to attack in absolute terms, and we thus turn our attention to explicitly training models for adversarial robustness, which we show to be a much more compute-efficient defense than scaling model size alone. In this setting, we also observe that adversarially trained larger models generalize faster and better to modified attacks not seen during training when compared with smaller models. Finally, we analyze the offense/defense balance of increasing compute, finding parity in some settings and an advantage for offense in others, suggesting that adversarial training alone is not sufficient to solve robustness, even at greater model scales.

2024-07-25

ArXiv (preprint)

Do Transformer World Models Give Better Policy Gradients?

Michel Ma

Tianwei Ni

Clement Gehring

Pierluca D'Oro

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (published)

Exploring Scaling Trends in LLM Robustness

Nikolaus H. R. Howe

Michał Zając

Ian R. McKenzie

Oskar John Hollinsworth

Tom Tseng

Aaron David Tucker

Adam Gleave

Language model capabilities predictably improve from scaling a model's size and training data. Motivated by this, increasingly large languag… (see more)e models have been trained, yielding an array of impressive capabilities. Yet these models are vulnerable to adversarial prompts, such as"jailbreaks"that hijack models to perform undesired behaviors, posing a significant risk of misuse. Prior work indicates that computer vision models become more robust with model and data scaling, raising the question: does language model robustness also improve with scale? We study this question empirically, finding that larger models respond substantially better to adversarial training, but there is little to no benefit from model scale in the absence of explicit defenses.

2024-06-28

ICML.cc/2024/Workshop/NextGenAISafety (poster)

Generative Active Learning for the Search of Small-molecule Protein Binders

Maksym Korablyov

Cheng-Hao Liu

Moksh J. Jain

Almer M. van der Sloot

Eric Jolicoeur

Edward Ruediger

Andrei Cristian Nica

Emmanuel Bengio

Kostiantyn Lapchevskyi

Daniel St-Cyr

Doris Alexandra Schuetz

Victor I Butoi

Jarrid Rector-Brooks

Simon R. Blackburn

Leo Feng

Hadi Nekoei

Sai Krishna Gottipati

Priyesh Vijayan

Prateek Gupta

Ladislav Rampášek … (see 14 more)

Sasikanth Avancha

William L. Hamilton

Brooks Paige

Sanchit Misra

Stanisław Jastrzębski

Bharat Kaul

José Miguel Hernández-Lobato

Marwin Segler

Michael M. Bronstein

Anne Marinier

Mike Tyers

Yoshua Bengio

Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exh… (see more)ibit a property of interest remains a significant challenge. We introduce LambdaZero, a generative active learning approach to search for synthesizable molecules. Powered by deep reinforcement learning, LambdaZero learns to search over the vast space of molecules to discover candidates with a desired property. We apply LambdaZero with molecular docking to design novel small molecules that inhibit the enzyme soluble Epoxide Hydrolase 2 (sEH), while enforcing constraints on synthesizability and drug-likeliness. LambdaZero provides an exponential speedup in terms of the number of calls to the expensive molecular docking oracle, and LambdaZero de novo designed molecules reach docking scores that would otherwise require the virtual screening of a hundred billion molecules. Importantly, LambdaZero discovers novel scaffolds of synthesizable, drug-like inhibitors for sEH. In in vitro experimental validation, a series of ligands from a generated quinazoline-based scaffold were synthesized, and the lead inhibitor N-(4,6-di(pyrrolidin-1-yl)quinazolin-2-yl)-N-methylbenzamide (UM0152893) displayed sub-micromolar enzyme inhibition of sEH.

2024-05-02

ArXiv (preprint)

Bridging State and History Representations: Understanding Self-Predictive RL

Tianwei Ni

Benjamin Eysenbach

Erfan SeyedSalehi

Michel Ma

Clement Gehring

Aditya Mahajan

Representations are at the core of all deep reinforcement learning (RL) methods for both Markov decision processes (MDPs) and partially obse… (see more)rvable Markov decision processes (POMDPs). Many representation learning methods and theoretical frameworks have been developed to understand what constitutes an effective representation. However, the relationships between these methods and the shared properties among them remain unclear. In this paper, we show that many of these seemingly distinct methods and frameworks for state and history abstractions are, in fact, based on a common idea of self-predictive abstraction. Furthermore, we provide theoretical insights into the widely adopted objectives and optimization, such as the stop-gradient technique, in learning self-predictive representations. These findings together yield a minimalist algorithm to learn self-predictive representations for states and histories. We validate our theories by applying our algorithm to standard MDPs, MDPs with distractors, and POMDPs with sparse rewards. These findings culminate in a set of preliminary guidelines for RL practitioners.

2024-01-17

ArXiv (preprint)