Sparse Memory Finetuning as a Low-Forgetting Alternative to LoRA and Full Finetuning
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 L...
| Authors | Prakhar Gupta, Garv Shah, Satyam Goyal et al. |
|---|---|
| Primary category | cs.CL |
| Published | 4 May 2026 |
| Last updated at source | 8 June 2026 |
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