arXiv Machine Learning

Realistic Continual Learning Approach using Pre-trained Models

arXiv Machine Learning

Jul 10

When Does Continual Learning Require Learning

arXiv:2607. 07847v1 Announce Type: new Abstract: As large language models (LLMs) become increasingly capable, the next question is how can we enable models to continually learn?

By Anne Harrington, Nayan Saxena, Michael Murphy, Anastasia Borovykh, Zeyu Yun, Sridhar Kamath, Ara Eindra Kyi, Trevor Darrell, Jitendra Malik, Yutong Bai

llms agents reinforcement-learning fine-tuning efficiency benchmarks

arXiv Machine Learning

2d ago

CLARE: Scalable Class-Incremental Continual Learning via a Sparsity-Based Framework

arXiv:2609.17026v1 Announce Type: new Abstract: Continual learning must balance the learning of new knowledge with the retention of previously learned knowledge to incrementally learn tasks from a da...

By Yunxiang Fu, Meng Lou, Zicheng Liao, Yizhou Yu

fine-tuning efficiency benchmarks

arXiv Machine Learning

Jul 28

Gradient-Free Continual Learning

arXiv:2504. 01219v2 Announce Type: replace Abstract: Neural networks are notorious for forgetting old skills when taught new ones - a problem known as catastrophic forgetting.

By Grzegorz Rype\'s\'c

benchmarks safety

arXiv AI

Aug 11

Beyond Static Models: An Evolving Framework for Continual Learning in Large Language Models across Training Stages

arXiv:2603. 12658v2 Announce Type: replace-cross Abstract: Continual learning (CL) has emerged as a pivotal paradigm to enable large language models (LLMs) to dynamically adapt to evolving knowledge and sequential tasks while mitigating catastrophic forgetting, a critical limitation of the static pre-training paradigm inherent to modern LLMs.

By Hongyang Chen, Zhongwu Sun, Hongfei Ye, Kunchi Li, Xuemin Lin

llms fine-tuning benchmarks safety

arXiv AI

Jun 17

Position: Modular Memory is the Key to Continual Learning Agents

arXiv:2603. 01761v2 Announce Type: replace-cross Abstract: Foundation models have transformed machine learning through large-scale pretraining and increased test-time compute.

By Vaggelis Dorovatas, Malte Schwerin, Andrew D. Bagdanov, Lucas Caccia, Antonio Carta, Laurent Charlin, Barbara Hammer, Tyler L. Hayes, Timm Hess, Christopher Kanan, Dhireesha Kudithipudi, Xialei Liu, Vincenzo Lomonaco, Jorge Mendez-Mendez, Darshan Patil, Ameya Prabhu, Elisa Ricci, Tinne Tuytelaars, Gido M. van de Ven, Liyuan Wang, Joost van de Weijer, Jonghyun Choi, Martin Mundt, Rahaf Aljundi

llms agents

arXiv Machine Learning

Jun 2

Turning Back Without Forgetting: Selective Backward Refinement for Parameter-Efficient Continual Learning

arXiv:2606. 01379v1 Announce Type: new Abstract: While prompt-based parameter-efficient continual learning mitigates catastrophic forgetting by isolating task-specific prompts, this isolation also limits later tasks from improving earlier ones, leaving backward knowledge transfer underexplored.

By Anushka Tiwari, Kaiyi Ji

llms benchmarks

arXiv Machine Learning

Jul 7

The Bayesian Approach to Continual Learning: An Overview

arXiv:2507. 08922v3 Announce Type: replace-cross Abstract: Continual learning is an online paradigm where a learner continually accumulates knowledge from different tasks encountered over sequential time steps.

By Tameem Adel

fine-tuning benchmarks

arXiv Machine Learning

Jun 9

Evaluating the Impact of Task Granularity on Catastrophic Forgetting in Continual Learning

arXiv:2606. 08013v1 Announce Type: new Abstract: Catastrophic forgetting, the abrupt loss of previously acquired knowledge upon learning new information, remains the central challenge in Continual Learning.

By Emre Alyamac, Himanshu Janmeda, Shashwat Krishna, Yash Vijay

arXiv AI

Aug 13

Representation Finetuning for Continual Learning

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

fine-tuning benchmarks

arXiv AI

Jul 22

Parameter-Efficient Continual Fine-Tuning: A Survey

arXiv:2504. 13822v3 Announce Type: replace-cross Abstract: The emergence of large pre-trained networks has revolutionized the AI field, unlocking new possibilities and achieving unprecedented performance.

By Eric Nuertey Coleman, Luigi Quarantiello, Ziyue Liu, Qinwen Yang, Samrat Mukherjee, Julio Hurtado, Vincenzo Lomonaco

fine-tuning benchmarks

arXiv Machine Learning

Aug 12

GLAM: Efficient Continual Learning at Scale via Grouped LoRA Adapter Merging

arXiv:2509. 13211v4 Announce Type: replace Abstract: The ability to learn continuously over time remains a major challenge for modern machine learning systems, even in the era of Foundation Models.

By Irene Testa, Luigi Quarantiello, Eric Nuertey Coleman, Samrat Mukherjee, Julio Hurtado, Vincenzo Lomonaco

fine-tuning benchmarks

arXiv Machine Learning

Jul 20

Rethinking Transfer in Continual Learning: A Replay-Based Realisation

arXiv:2607. 15587v1 Announce Type: new Abstract: Continual learning studies how deployed language models can continually acquire new tasks without expensive retraining from scratch.

By Yang Meng, Zhenya Liu, Zhuokai Zhao, Yuxin Chen

llms efficiency

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