Matteo Merler

My research interests revolve around bridging current large-scale models (such as LLMs and VLMs) with other learning systems, such as Reinforcement Learning (RL) and Symbolic Planning (SP). Specifically, I believe that a key strength of RL is the ability to learn from experience while interacting with the world, which is something current LLMs lack. At the same time, pure RL requires the agent to learn about the environment from scratch in every new situation, and hardly generalizes to new tasks. Therefore, I am interested in developing AI agents that are able to understand natural language instructions and leverage the large amount of real-world knowledge encoded in LLMs, but at the same time adapt to situations that require more complex reasoning and planning, going beyond memorization and pattern matching, but rather understanding the world from direct experience.