arXiv:2605.03229v2 Announce Type: replace-cross Abstract: Adapting a pretrained language model to a new task often hurts the general capabilities it already had, a problem known as catastrophic forgetting. Sparse Memory Finetuning (SMF) tries to avoid this by adding key-value memory layers to the model and, on each training step, updating only the small set of memory rows that the current batch reads most heavily. We re-implement SMF on Qwen-2.5-0.5B-Instruct and compare it with LoRA and full finetuning on MedMCQA, a 4-choice medical exam task, using WikiText perplexity and TriviaQA accuracy a
The proliferation of increasingly large language models necessitates efficient finetuning methods to adapt them to specific tasks without incurring prohibitive computational costs or sacrificing broad capabilities.
This research addresses catastrophic forgetting, a significant hurdle in AI development, by offering a practical method for models to learn new tasks while retaining previous knowledge more effectively.
New finetuning techniques like Sparse Memory Finetuning provide a more efficient and less destructive alternative to existing methods, potentially accelerating specialized AI deployment and improving model utility.
- · AI developers
- · Specialized AI applications
- · Companies deploying custom LLMs
- · Inefficient full-model finetuning approaches
Reduced computational overhead and training time for adapting large language models to new tasks.
Faster development and iteration cycles for AI products across various domains, as models can be specialized more easily.
Lower barriers to entry for smaller organizations wishing to leverage and customize advanced AI models, fostering broader innovation.
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Read at arXiv cs.LG