@inproceedings{liao-etal-2026-grole,
    title = "{GROLE}: Instance-Level Group Relative Optimization for {L}o{RA} Experts in Incremental Learning",
    author = "Liao, Yongyi  and
      Lai, Wencan  and
      Fang, Jun  and
      Guo, Jinjin  and
      Zhang, Xiaohui  and
      Liu, Zhiyuan  and
      Liu, Chao  and
      Liu, Pengzhang  and
      Jiang, Qixia",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1952/",
    pages = "39170--39182",
    ISBN = "979-8-89176-395-1",
    abstract = "While Large Language Models (LLMs) demonstrate remarkable zero-shot generalization, adapting them to downstream tasks or shifting data distributions often requires continual fine-tuning{---}a process prone to catastrophic forgetting and limited knowledge transfer. This challenge is especially pronounced in online Incremental Learning (IL) settings, where task boundaries are blurred, and data arrives in a non-stationary stream. To address these issues, we propose GROLE (Group Relative Optimization for LoRA Experts), a novel approach that incrementally constructs a pool of frozen, task-specific Low-Rank Adaptation (LoRA) experts. At its core, GROLE employs a lightweight, instance-level expert selector optimized through a group relative reinforcement learning objective, which dynamically combines relevant experts to maximize adaptability without compromising stability. Extensive experiments across diverse incremental learning benchmarks show that GROLE consistently outperforms state-of-the-art methods, particularly in task-free and blurred-boundary settings, achieving an optimal balance between plasticity and robustness."
}