143 citations · 308 across the 55 of their papers we have counts for
10 papers · 1 filter
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…