@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T GROLE: Instance-Level Group Relative Optimization for LoRA Experts in Incremental Learning
%A Liao, Yongyi
%A Lai, Wencan
%A Fang, Jun
%A Guo, Jinjin
%A Zhang, Xiaohui
%A Liu, Zhiyuan
%A Liu, Chao
%A Liu, Pengzhang
%A Jiang, Qixia
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F liao-etal-2026-grole
%X 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.
%U https://aclanthology.org/2026.findings-acl.1952/
%P 39170-39182
Markdown (Informal)
[GROLE: Instance-Level Group Relative Optimization for LoRA Experts in Incremental Learning](https://aclanthology.org/2026.findings-acl.1952/) (Liao et al., Findings 2026)
ACL
- Yongyi Liao, Wencan Lai, Jun Fang, Jinjin Guo, Xiaohui Zhang, Zhiyuan Liu, Chao Liu, Pengzhang Liu, and Qixia Jiang. 2026. GROLE: Instance-Level Group Relative Optimization for LoRA Experts in Incremental Learning. In Findings of the Association for Computational Linguistics: ACL 2026, pages 39170–39182, San Diego, California, United States. Association for Computational Linguistics.