Skill Localization

Skill localization research aims to identify and isolate the specific parts of a model (e.g., parameters, neurons, or code segments) responsible for performing particular tasks or "skills." Current work focuses on applying this concept to various machine learning models, including reinforcement learning agents and large language models, often employing techniques like task decomposition, parameter-efficient fine-tuning, and group-wise skill identification to achieve efficient knowledge transfer and prevent catastrophic forgetting in continual learning scenarios. This research is significant because it enhances model efficiency, improves understanding of model internal workings, and facilitates better knowledge transfer across tasks, leading to more robust and adaptable AI systems.