Unlocking Continual Learning in Machine Learning: Introducing Nested Learning
In the last decade, machine learning (ML) has rapidly evolved, thanks to advanced neural network architectures and innovative training algorithms. Despite the success of large language models (LLMs), challenges—particularly in continual learning—remain a significant hurdle.
Understanding Continual Learning
Continual learning refers to a model’s ability to acquire new knowledge and skills over time without losing previously learned information. While the human brain exemplifies this ability through neuroplasticity, current LLMs often struggle with limitations similar to anterograde amnesia, as they can only retain information from their immediate input context.
The Dilemma of Catastrophic Forgetting
One primary issue in continual learning is catastrophic forgetting (CF). This phenomenon occurs when a model updates its parameters with new data, leading to a decline in performance on previously learned tasks. To counter CF, researchers have commonly employed architectural designs or improved optimization rules. However, treating these two aspects as separate entities has hindered the creation of a cohesive learning system.
Introducing Nested Learning
In our recent paper, “Nested Learning: The Illusion of Deep Learning Architectures”, presented at NeurIPS 2025, we propose a groundbreaking concept called Nested Learning. This approach redefines a single ML model not as a linear process, but as an interconnected system of multi-level learning problems optimized simultaneously. By recognizing that the model’s architecture and its training rules are fundamentally related, we unlock a new dimension of AI design that enhances learning efficiency and addresses catastrophic forgetting.
The Hope Architecture: A Proof of Concept
To validate Nested Learning, we developed a self-modifying architecture named Hope. The “Hope” model showcases superior performance in language modeling and improved long-context memory management, surpassing existing state-of-the-art models.
Conclusion
Nested Learning offers an innovative approach to solving the challenges of continual learning in machine learning. By unifying architectural design and optimization, we lay the groundwork for more capable and adaptive AI systems.
Related Keywords
- Machine Learning
- Large Language Models
- Continual Learning
- Catastrophic Forgetting
- Neural Networks
- Neuroplasticity
- AI Design
