Paper says selective parameter freezing may cut lifelong pretraining costs
ttkciar · reddit · 2026-08-04
A paper proposes selective parameter suppression to reduce catastrophic forgetting
The paper, Towards Scalable Lifelong Knowledge Editing with Selective Knowledge Suppression, studies continued pretraining without catastrophic forgetting.
- The core idea is to identify parameters that can be changed safely while preserving targeted concepts, and freeze the rest.
- The authors argue that current practice — mixing new data into large legacy corpora — works, but makes training an order of magnitude or more more expensive because old data must be replayed to reinforce prior knowledge.
- Their method could make that data-mixing step unnecessary, allowing models to train only on new data while retaining old knowledge more safely.
- The Reddit post links the paper at arXiv:2604.19089v1.
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