Research Topic
Robust Reinforcement Learning for Large Language Models: Geometry-Regulated Policy Optimization, Process Reward Integration, and Agentic AI.
Supervisors
- Professor Sahar Vahdati (Leibniz University Hannover )
- Professor Jens Lehmann (Dresden University of Technology)
Thesis Abstract
My research develops robust reinforcement learning methods for large language models by improving training stability, reasoning accuracy, and efficiency. It introduces geometry-regulated policy optimization and verifiable process rewards to reduce reward hacking while enhancing step-by-step reasoning. The project also extends reinforcement learning to tool-using AI agents and investigates efficient inference methods for faster and more cost-effective language model deployment.
Publications
- Mohammad Rezaei, Jens Lehmann, and Sahar Vahdati. “LLM Reasoning with Process Rewards for Outcome-Guided Steps”. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN). Presented at IJCNN 2026. 2026. url: arxiv.org/abs/2604.02341.
Conference Presentations
- International Joint Conference on Neural Networks (IJCNN 2026): LLM Reasoning with Process Rewards for Outcome-Guided Steps