3 papers
cs.IT2026
Multi-User Diversity Scaling in Heavy-Tailed Fading
Yonathan Murin, Ali Ãzer Ercan, Nariman Farsad
Classical multi-user diversity theory predicts that throughput over Rayleigh fading channels grows as . In this work, we demonstrate a fundamental shift in this s…
cs.RO2025
Video-Language Critic: Transferable Reward Functions for Language-Conditioned Robotics
Minttu Alakuijala, Reginald McLean, Isaac Woungang +4
Natural language is often the easiest and most convenient modality for humans to specify tasks for robots. However, learning to ground language to behavior typically requires impra…
cs.LG2025
Multi-Task Reinforcement Learning Enables Parameter Scaling
Reginald McLean, Evangelos Chatzaroulas, Jordan Terry +3
Multi-task reinforcement learning (MTRL) aims to endow a single agent with the ability to perform well on multiple tasks. Recent works have focused on developing novel sophisticate…