Multimodal Reasoning and Agentic Learning for Vision-Language Models

This doctoral research investigates multimodal foundation models, visual reasoning, and agentic reinforcement learning. The research involves the use of public datasets, synthetically generated training data, model interaction trajectories, experimental configurations, evaluation outputs, and machine learning model checkpoints.