6 papers
MMAI Gym for Science: Training Liquid Foundation Models for Drug Discovery
Maksim Kuznetsov, Zulfat Miftahutdinov, Rim Shayakhmetov +17
General-purpose large language models (LLMs) that rely on in-context learning do not reliably deliver the scientific understanding and performance required for drug discovery tasks…
CoPeP: Benchmarking Continual Pretraining for Protein Language Models
Darshan Patil, Pranshu Malviya, Mathieu Reymond +2
Protein language models (pLMs) have recently gained significant attention for their ability to uncover relationships between sequence, structure, and function from evolutionary sta…
JEF-Hinter: Leveraging Offline Knowledge for Improving Web Agents Adaptation
Hadi Nekoei, Aman Jaiswal, Patrice Bechard +7
Large language model (LLM) agents perform well in sequential decision-making tasks, but improving them on unfamiliar domains often requires costly online interactions or fine-tunin…
Squeezing More from the Stream : Learning Representation Online for Streaming Reinforcement Learning
Nilaksh, Antoine Clavaud, Mathieu Reymond +2
In streaming Reinforcement Learning (RL), transitions are observed and discarded immediately after a single update. While this minimizes resource usage for on-device applications,…
GRPO-: Credit Assignment improves LLM Reasoning
Prasanna Parthasarathi, Mathieu Reymond, Boxing Chen +2
Large language models (LLMs) are increasingly deployed for tasks requiring complex reasoning, prompting significant interest in improving their reasoning abilities through post-tra…
CrystalGym: A New Benchmark for Materials Discovery Using Reinforcement Learning
Prashant Govindarajan, Mathieu Reymond, Antoine Clavaud +3
In silico design and optimization of new materials primarily relies on high-accuracy atomic simulators that perform density functional theory (DFT) calculations. While recent works…