2025
Breaking the Performance Ceiling in Reinforcement Learning requires Inference Strategies
Felix Chalumeau, Daniel Rajaonarivonivelomanantsoa, Ruan John de Kock, Juan Claude Formanek, Sasha Abramowitz, Omayma Mahjoub +8
NeurIPS 2025oral
Reinforcement learning (RL) systems have countless applications, from energy-grid management to protein design. However, such real-world scenarios are often extremely difficult, combinatorial in nature, and require complex coordination between multiple agents. This level of complexity can cause even…