[2605.03229] Sparse Memory Finetuning as a Low-Forgetting Alternative to LoRA and Full Finetuning
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Computer Science > Computation and Language
arXiv:2605.03229 (cs)
[Submitted on 4 May 2026]
Title:Sparse Memory Finetuning as a Low-Forgetting Alternative to LoRA and Full Finetuning
Authors:Prakhar Gupta, Garv Shah, Satyam Goyal, Anirudh Kanchi
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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 as forgetting probes. SMF improves MedMCQA by 2.5 percentage points while keeping both forgetting probes within roughly 1 point of the base model, whereas LoRA and full finetuning achieve larger gains but with clear drift on both. We also compare two row-selection rules (KL-divergence and TF-IDF), which balance the two forgetting metrics differently.
Subjects:
Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2605.03229 [cs.CL]
(or arXiv:2605.03229v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.03229
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Prakhar Gupta [view email] [v1] Mon, 4 May 2026 23:46:40 UTC (1,325 KB)
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