arXiv Machine Learning July 10, 2026 By Rapha\"el Bayle, Martial Mermillod, Robert M. French

Are Current Continual Learning Methods Truly Agnostic? Introducing OPRE, a Step Toward Agnostic Continual Learning

Read the original on arXiv Machine Learning →

arXiv:2511. 08226v2 Announce Type: replace Abstract: In order to achieve Continual Learning (CL), the problem of catastrophic forgetting, one that has plagued neural networks since their inception, must be overcome.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

benchmarks

arXiv AI

Jun 8

Towards Efficient and Exact Forgetting Services in Pre-Trained-Model-based Continual Learning

arXiv:2505. 12239v2 Announce Type: replace-cross Abstract: In Continual Learning (CL), using a Pre-Trained Model (PTM) as the feature extractor has become a popular practice.

By Yajiang Huang, Jianheng Tang, Kejia Fan, Huiping Zhuang, Anfeng Liu, Tian Wang, Yunhuai Liu, Mianxiong Dong, Houbing Herbert Song

benchmarks safety

arXiv Machine Learning

Jun 30

The Forgetting-Retention Dilemma: Certified Unlearning Theory in Continual Learning

arXiv:2606. 29832v1 Announce Type: new Abstract: Machine unlearning aims to eliminate the influence of specific data from trained models to safeguard privacy.

By Yiting Hu, Lingjie Duan, Qian Zhang

safety

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

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

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 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

Related stories

Towards Efficient and Exact Forgetting Services in Pre-Trained-Model-based Continual Learning

The Forgetting-Retention Dilemma: Certified Unlearning Theory in Continual Learning

Parameter-Efficient Continual Fine-Tuning: A Survey

When Does Continual Learning Require Learning

The Bayesian Approach to Continual Learning: An Overview

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