arXiv:2608. 12332v1 Announce Type: cross Abstract: In recent years, low-rank adaptation (LoRA) has emerged as a significant paradigm that freezes pre-trained weights and introduces small, learnable adapters instead of fine-tuning the full set of parameters.
By Hyowon Wi, Noseong Park
arXiv:2603. 11201v3 Announce Type: replace-cross Abstract: The world is inherently dynamic, and continual learning aims to enable models to adapt to ever-evolving data streams.
By Haihua Luo, Xuming Ran, Tommi K\"arkk\"ainen, Huiyan Xue, Zhonghua Chen, Qi Xu, Fengyu Cong
The paper introduces Foundation Preserving LoRA (FoLoRA), a forgetting‑aware optimization framework that balances adaptation to downstream tasks with preservation of pretraining capabilities. FoLoRA uses a first‑order preservation condition to define a forgetting penalty based on pretraining‑proxy activations and a task utility from downstream activations, scoring update directions via a generalized Rayleigh quotient. This spectral coordinate system enables gated Adam updates that reduce low‑utility, high‑penalty directions, and the method constructs pretraining proxy calibration data by sampling from the pretrained model. Experiments on math, code, and instruction‑following tasks demonstrate that FoLoRA achieves a stronger balance between target task performance and aggregate preservation of non‑target capabilities compared to baselines.
By Dongjun Kim, Adrian de Wynter, Huancheng Chen, Heasung Kim, Haris Vikalo
arXiv:2607. 09202v1 Announce Type: cross Abstract: Continual learning commonly relies on post-hoc mechanisms such as replay, elastic regularization, or distillation.
By Julius St\"ork
arXiv:2608.21487v1 Announce Type: cross
Abstract: Vision-Language Models (VLMs) exhibit strong zero-shot capabilities, making them an attractive solution for continual learning across diverse tasks....
By Chang Sun, Francesco Barbato, Matteo Caligiuri, Pietro Zanuttigh
The paper introduces EoupCT, a framework that estimates and orthogonalizes unknown pre‑training gradients to mitigate catastrophic forgetting during continual fine‑tuning of large language models. It generates pseudo data most susceptible to forgetting using a learnable soft prompt with Gumbel‑Softmax, then jointly optimizes model parameters and the prompt via a first‑order Pareto optimizer to enforce orthogonality between new task updates and the estimated gradients. Experiments on multiple LLMs show that EoupCT preserves both task‑specific performance and the models’ inherent general‑purpose knowledge.
By Bing Wang, Changchun Li, Xin-Qiang Cai, Lin Yuanbo Wu, Ximing Li, Gang Niu, Masashi Sugiyama