143 citations · 308 across the 55 of their papers we have counts for
60 papers
From Static Policies to Adaptive Priors in Offline Reinforcement Learning
Tianwei Ni, Vineet Jain, Akash Karthikeyan +1
Offline reinforcement learning (RL) has traditionally focused on learning policies for direct deployment under conservative objectives, where uncertainty outside the offline datase…
Do Tabular Foundation Models Know Physics? Contamination, Units, and the Deterministic Limit
Wassim Tenachi, Yashar Hezaveh, Laurence Perreault Levasseur +1
Tabular foundation models (TFMs) learn to fill in tables the way language models fill in text --- and tables are arguably the format in which most physical measurement arrives. Did…
Building2Building: A Large Scale Benchmark for Generalizable Real-World Reinforcement Learning
Vincent Taboga, Justin Veilleux, Doseok Jang +2
Reinforcement learning (RL) has achieved strong results in control, yet learned policies remain brittle to changes in dynamics, action spaces, observation spaces, or goals, a criti…
Beyond One-Size-Fits-All: Diagnosis-Driven Online Reinforcement Learning with Offline Priors
Guozheng Ma, Lu Li, Zilin Wang +2
Online reinforcement learning (RL) agents increasingly depend on knowledge acquired offline to achieve practical efficiency. Originally studied in offline-to-online RL, this paradi…
Representation Learning Enables Scalable Multitask Deep Reinforcement Learning
Johan Obando-Ceron, Lu Li, Scott Fujimoto +3
Scaling reinforcement learning (RL) to diverse multitask settings remains a central challenge. While recent advances in model-based RL achieve strong performance, they rely on plan…
Rotation-Preserving Supervised Fine-Tuning
Hangzhan Jin, Tianwei Ni, Lu Li +3
Supervised fine-tuning (SFT) improves in-domain performance but can degrade out-of-domain (OOD) generalization. Prior work suggests that this degradation is related to changes in d…
Layerwise LQR for Geometry-Aware Optimization of Deep Networks
Simon Dufort-Labbé, Pierre-Luc Bacon, Razvan Pascanu +2
Geometry-aware optimizers such as Newton and natural gradient can improve conditioning in deep learning, but scalable variants such as K-FAC, Shampoo, and related preconditioners u…
Towards Practical World Model-based Reinforcement Learning for Vision-Language-Action Models
Zhilong Zhang, Haoxiang Ren, Yihao Sun +6
Vision-Language-Action (VLA) models show strong generalization for robotic control, but finetuning them with reinforcement learning (RL) is constrained by the high cost and safety…
What Makes Value Learning Efficient in Residual Reinforcement Learning?
Guozheng Ma, Lu Li, Haoyu Wang +3
Residual reinforcement learning (RL) enables stable online refinement of expressive pretrained policies by freezing the base and learning only bounded corrections. However, value l…
Reward Redistribution for CVaR MDPs using a Bellman Operator on L-infinity
Aneri Muni, Vincent Taboga, Esther Derman +2
Tail-end risk measures such as static conditional value-at-risk (CVaR) are used in safety-critical applications to prevent rare, yet catastrophic events. Unlike risk-neutral object…
Long-Horizon Model-Based Offline Reinforcement Learning Without Explicit Conservatism
Tianwei Ni, Esther Derman, Vineet Jain +3
Popular offline reinforcement learning (RL) methods rely on explicit conservatism, penalizing out-of-dataset actions or restricting rollout horizons. We question the universality o…
The Three Regimes of Offline-to-Online Reinforcement Learning
Lu Li, Tianwei Ni, Yihao Sun +1
Offline-to-online reinforcement learning (RL) has emerged as a practical paradigm that leverages offline datasets for pretraining and online interactions for fine-tuning. However,…
Planning with Unified Multimodal Models
Yihao Sun, Zhilong Zhang, Yang Yu +1
With the powerful reasoning capabilities of large language models (LLMs) and vision-language models (VLMs), many recent works have explored using them for decision-making. However,…
Discovery of Sustainable Refrigerants through Physics-Informed RL Fine-Tuning of Sequence Models
Adrien Goldszal, Diego Calanzone, Vincent Taboga +1
Most refrigerants currently used in air-conditioning systems, such as hydrofluorocarbons, are potent greenhouse gases and are being phased down. Large-scale molecular screening has…
Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning
Guozheng Ma, Lu Li, Zilin Wang +3
Effectively scaling up deep reinforcement learning models has proven notoriously difficult due to network pathologies during training, motivating various targeted interventions suc…
Discrete Compositional Generation via General Soft Operators and Robust Reinforcement Learning
Marco Jiralerspong, Esther Derman, Danilo Vucetic +5
A major bottleneck in scientific discovery consists of narrowing an exponentially large set of objects, such as proteins or molecules, to a small set of promising candidates with d…
Stable Gradients for Stable Learning at Scale in Deep Reinforcement Learning
Roger Creus Castanyer, Johan Obando-Ceron, Lu Li +4
Scaling deep reinforcement learning networks is challenging and often results in degraded performance, yet the root causes of this failure mode remain poorly understood. Several re…
State Entropy Regularization for Robust Reinforcement Learning
Yonatan Ashlag, Uri Koren, Mirco Mutti +3
State entropy regularization has empirically shown better exploration and sample complexity in reinforcement learning (RL). However, its theoretical guarantees have not been studie…
Understanding Behavioral Metric Learning: A Large-Scale Study on Distracting Reinforcement Learning Environments
Ziyan Luo, Tianwei Ni, Pierre-Luc Bacon +2
A key approach to state abstraction is approximating behavioral metrics (notably, bisimulation metrics) in the observation space and embedding these learned distances in the repres…
Mol-MoE: Training Preference-Guided Routers for Molecule Generation
Diego Calanzone, Pierluca D'Oro, Pierre-Luc Bacon
Recent advances in language models have enabled framing molecule generation as sequence modeling. However, existing approaches often rely on single-objective reinforcement learning…
MaestroMotif: Skill Design from Artificial Intelligence Feedback
Martin Klissarov, Mikael Henaff, Roberta Raileanu +7
Describing skills in natural language has the potential to provide an accessible way to inject human knowledge about decision-making into an AI system. We present MaestroMotif, a m…
Scaling Trends in Language Model Robustness
Nikolaus Howe, Ian McKenzie, Oskar Hollinsworth +5
Increasing model size has unlocked a dazzling array of capabilities in modern language models. At the same time, even frontier models remain vulnerable to jailbreaks and prompt inj…
Decoupling regularization from the action space
Sobhan Mohammadpour, Emma Frejinger, Pierre-Luc Bacon
Regularized reinforcement learning (RL), particularly the entropy-regularized kind, has gained traction in optimal control and inverse RL. While standard unregularized RL methods r…
Generative Active Learning for the Search of Small-molecule Protein Binders
Maksym Korablyov, Cheng-Hao Liu, Moksh Jain +31
Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exhibit a property of interest remains a si…