Large, unlabeled datasets of prior experience are becoming more common for agentic tasks in domains such as robotics and coding. A typical approach is to train a policy on this data, then continue with further steps—though the abstract cuts off before detailing what those steps are.
The paper, titled "Measuring Empowerment from Offline Data for Intrinsically Motivated Exploration," appears to address how to quantify empowerment—an agent's capacity to influence its environment—using only static datasets. This could guide exploration without requiring costly online interaction.
The provided abstract only establishes the motivation; the specific method and results are not included in the source. Still, the title and opening suggest a practical measure of empowerment from offline data, which would be valuable for exploration in settings where online data collection is expensive.