Google Research has introduced nested learning, a new machine-learning paradigm aimed at continual learning. The announcement frames it as a distinct approach within the field, and it is published under the Algorithms & Theory category, suggesting a focus on foundational model design rather than a specific application.
Continual learning concerns how models adapt to new tasks or data over time without discarding earlier knowledge. Nested learning appears to offer a structural way of thinking about that process, though the blog announcement itself does not provide implementation details or experimental results.
Because there is only one source, there are no conflicting accounts to note. The summary here is limited to what Google Research has stated in its own announcement.