Researchers highlight the limitations of current large language models (LLMs) due to "catastrophic forgetting," where models overwrite learned information. To combat this, they propose Nested Learning, which treats models as interconnected optimization challenges. This approach allows for deeper computational structures, thereby enhancing knowledge retention. As the team noted, "Each internal problem has its own context," suggesting that this could lead to more efficient learning algorithms and advance AI towards continual learning capabilities.