Continual Learning via Sparse Memory Finetuning (Paper Review)

Author(s): Hira Ahmad

Originally published on Towards AI.

Continual Learning via Sparse Memory Finetuning (Paper Review)

Modern large language models learn vast amounts of knowledge; yet when we try to teach them something new, they tend to forget what they already know. This challenge, known as catastrophic forgetting, limits how flexibly models can adapt over time. Meta AI’s Sparse Memory Finetuning introduces a clever, lightweight solution: instead of changing the entire model, it teaches selectively updating only the few memory slots that truly matter.

The article explores the concept of Sparse Memory Finetuning, highlighting how this method allows large language models to retain knowledge while learning new information, effectively addressing the issue of catastrophic forgetting. It describes experimental setups comparing this approach to traditional finetuning techniques, demonstrating its superiority in memory efficiency and retention. By analyzing memory accesses in various factual learning tasks, the authors show that models can learn incrementally without compromising stability, paving the way for more effective continual learning methodologies in AI.

Read the full blog for free on Medium.

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Published via Towards AI