Mehdi Fatemi

Mehdi Fatemi is a Principal Research Scientist at Snorkel AI and a reinforcement learning (RL) researcher with more than 15 years of experience, beginning with his PhD at McMaster University. His work spans the foundations and applications of RL, including value-based methods, long-horizon and multi-turn decision-making, and, over the past few years, reasoning in large language models under diverse feedback signals and uncertainty. Previously, during several years at Microsoft Research, he made numerous core contributions to RL through papers, patents, and public releases, including Hybrid Reward Architecture for learning from decomposable rewards and dead-end theory for identifying and avoiding irrecoverable states. His recent work includes Concise Reasoning, which shows that RL’s loss alone drives reasoning models toward verbosity or conciseness, depending on the solvability of the training tasks.