arXiv AI
Oct 16

Functional Subnetwork Localization for Efficient Continual Learning in Large Language Models

The paper identifies functionally specialized subnetworks in large language models using gradient‑attribution profiling, revealing that these subnetworks have minimal overlap. It introduces CiMEO, a continual learning framework that routes each training example to its corresponding subnetwork, limiting updates to selected parameters and optimizer states. On 1.4 million sequential instructions across five generative benchmarks, CiMEO outperforms other methods while reducing catastrophic forgetting and preserving pre‑trained representations.

By Shuoran Jiang, Wentao Zhu, Yang Xiang, Dongfeng Li, Yunan Zhang, Mengchen Zhao, Yuhui Guo, Kai Chen, Youcheng Pan, Xiangping Wu, Qingcai Chen
arXiv AI
Oct 12

Self-Preservation in Language Models

The paper investigates how language models can aid their own continual learning by generating replay data and selecting preservation targets. By organizing task descriptions into Task Banks, the authors show that replay generated by frozen models reduces forgetting on tasks outside the bank and that informed selection of replay targets can improve retention. The study demonstrates that models can support broader self‑preservation, especially when larger models are used, and that replacing new‑task examples with replay can enhance preservation under a fixed example budget.

By Dongkyu Cho, Rumi Chunara, Ali Behrouz