Google Introduces HOPE: A Self-Modifying AI Model with Continual Learning Capabilities
In a significant advancement toward creating AI systems that can continuously learn and improve, Google researchers have unveiled a new machine learning model featuring a self-modifying architecture. Known as HOPE, this model reportedly surpasses current state-of-the-art AI systems in handling long-context memory.
Google described HOPE as a proof-of-concept for an innovative approach called Nested Learning, which treats a single AI model not as one continuous learning process, but as a collection of interconnected, multi-level learning problems optimized at the same time. This framework, according to Google, could address key limitations of modern large language models (LLMs), especially their inability to learn continually—a capability viewed as essential for developing artificial general intelligence (AGI) or human-like intelligence.
The challenge of continual learning remains one of the biggest hurdles in AI. As AI scientist Andrej Karpathy recently noted, no existing system can yet learn continuously without external retraining. “They don’t have continual learning. You can’t just tell them something and they’ll remember it. They’re cognitively lacking,” he said, suggesting that solving this issue could take another decade.
Google’s researchers believe that Nested Learning provides a new theoretical foundation for overcoming this limitation. “We believe the Nested Learning paradigm offers a robust foundation for closing the gap between the limited, forgetting nature of current LLMs and the remarkable continual learning abilities of the human brain,” they wrote. Their findings were detailed in a paper titled “Nested Learning: The Illusion of Deep Learning Architectures,” presented at NeurIPS 2025.
Understanding Continual Learning
Current LLMs can generate poetry, write essays, and code in seconds, but they cannot learn from experience or retain new information without extensive retraining. Unlike the human brain—which builds on prior knowledge—LLMs suffer from a problem known as catastrophic forgetting (CF), where learning new data causes them to lose previously learned information.
Researchers have long tried to solve CF by modifying model architectures or optimization methods. Google’s team argues that both architecture and optimization are fundamentally intertwined, and understanding this relationship is key to developing models that can truly learn continuously.
What is Nested Learning?
Nested Learning redefines how machine learning systems are structured. Instead of viewing an AI model as a single learning process, it treats it as a network of interconnected optimization problems, each with its own context flow and data to learn from.
In this approach, multiple learning components operate either in parallel or hierarchically, allowing for deeper computational depth and more adaptive behavior. By integrating these nested systems, AI models can achieve richer, more stable, and more human-like learning abilities.
Google’s proof-of-concept model, HOPE, demonstrated lower perplexity and higher accuracy than leading LLMs on a variety of standard language modeling and reasoning benchmarks. According to the researchers, this result shows that Nested Learning could pave the way for more expressive, capable, and efficient AI systems in the future.
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