GeRe: Towards Efficient Anti-Forgetting in Continual Learning of LLM via
  General Samples Replay
Authors: name: Yunan Zhang, name: Shuoran Jiang, name: Mengchen Zhao, name: Yuefeng Li, name: Yang Fan, name: Xiangping Wu, name: Qingcai Chen

The continual learning capability of large language models (LLMs) is crucial
for advancing artificial general intelligence. However, continual fine-tuning
LLMs across various domains often suffers from catastrophic forgetting,
characterized by: 1) significant forgetting of their general capabilities, and
2) sharp performance declines in previously learned tasks. To simultaneously
address both issues in a simple yet stable manner, we propose General Sample
Replay (GeRe), a framework that use usual pretraining texts for efficient
anti-forgetting. Beyond revisiting the most prevalent replay-based practices
under GeRe, we further leverage neural states to introduce a enhanced
activation states constrained optimization method using threshold-based margin
(TM) loss, which maintains activation state consistency during replay learning.
We are the first to validate that a small, fixed set of pre-collected general
replay samples is sufficient to resolve both concerns--retaining general
capabilities while promoting overall performance across sequential tasks.
Indeed, the former can inherently facilitate the latter. Through controlled
experiments, we systematically compare TM with different replay strategies
under the GeRe framework, including vanilla label fitting, logit imitation via
KL divergence and feature imitation via L1/L2 losses. Results demonstrate that
TM consistently improves performance and exhibits better robustness. Our work
paves the way for efficient replay of LLMs for the future. Our code and data
are available at https://github.com/Qznan/GeRe.