Google's "Nested Learning" Aims to Revolutionize AI Self-Improvement

On November 7, 2025, Google researchers published a paper at the 39th Conference on Neural Information Processing Systems (NeurIPS 2025), outlining their groundbreaking "nested learning" (NL) approach to AI architecture. This new design aims to address the limitations of current generative AI and large language models (LLMs).

The NL approach, as demonstrated in Google's prototype named "Hope", features several key innovations. It employs continual learning, allowing the AI to continuously update and improve itself. The system also boasts deeper computational depth and interconnected multi-level layers that optimize simultaneously, rather than the static nature of many existing LLMs.

The researchers draw parallels between this AI architecture and the way humans learn, with the concept of "nested layers" of knowledge. Just as people build upon their initial understanding of a topic, the NL model is designed to expand its capabilities over time through self-improvement.

While the success of this new approach remains to be seen, the Google team's efforts represent a bold attempt to rethink the fundamental design of AI systems. By enabling continual learning and optimization, the researchers hope to pave the way for more advanced, self-improving AI that can better adapt to the complexities of the real world.