Continual Learning for LLMs

LLM: Large Language Model

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Overview

The field investigates how large language models can keep learning through continual pretraining, instruction tuning, alignment, and personalization without losing earlier knowledge or skills. Common approaches include replaying old or generated examples, distillation and regularization, selective or orthogonal parameter updates, modular adapters, and external memories that leave the model weights unchanged. Evaluations use sequential task suites such as TRACE and SuperNI, domain streams such as biomedical NLP, and evolving knowledge or user-interaction settings, measuring both new-task performance and retention over time. No single approach works reliably across settings: results depend on task order, model capacity, and update type, while long-term evaluation must balance adaptation, forgetting, transfer, safety, and compute cost.

Latest papers

CardsList

236 papers44 papers in the last four weeks, up 110% on the four weeks before. 0.3% of all new papers.

  1. Surprise Alone Learns Lies: Evidence-Gated Continual Learning for LLMs from Streaming Claims

    Oct 29, 2026Abdelkader Metales, Miriam GrabenheinrichContinual Learning for LLMsLLM Reliability

    caaise GmbH

  2. Preserving Safety During Sequential Fine-Tuning: A Factorial and Geometric Analysis of Replay

    Oct 27, 2026George Ruellan, Diana Benavides-PradoSafety-Preserving Fine-Tuning of Language ModelsSafety-Utility Trade-Off in Language Models

    School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom

  3. CaLLM: Continual Adaptation of LLMs via Non-Parametric Adapter Routing

    Oct 27, 2026Bilge Celik, Anna Vettoruzzo, Joaquin VanschorenContinual Learning for LLMsAdapter-Based Continual Learning

    Eindhoven University of Technology

  4. CAMR: Compatibility-Aware Memory Replay for Continual Learning in Large Language Models

    Oct 27, 2026Jingpu Yang, Tianhao Li, Fengxian Ji +3Continual LearningContinual Learning for LLMs

    Beijing Zhongguancun Academy · Northeastern University · Beihang University +1

  5. SHIFT-JEV: Certified Abstention and Gain-Gated Continual Adaptation for LLM-Based Decision Models under Distribution Shift

    Oct 25, 2026Muhammad Atif Saeed, Azka Atiq, Adarsh Kumar +3Online Language Model AdaptationSelective Prediction

    Department of AI & Data Science, National University of Computer & Emerging Sciences, Islamabad, Pakistan · Data Engineer, University of Arkansas at Little Rock, Arkansas, USA · Department of Computer Science, National University of Computer & Emerging Sciences, Islamabad, Pakistan +2

  6. Entropic Control for Homeostatic Optimization: Pre-empting Catastrophic Forgetting via Dynamic Feedback

    Oct 25, 2026Shangxi Wu, Xiaorong Yan, Xiaojun Meng +2Catastrophic ForgettingContinual Learning for LLMs

    Beijing Jiaotong University · The Hong Kong Polytechnic University · Huawei Noah’s Ark Lab

  7. FedSEAM: Federated Continual Learning of LLMs via Subspace Expert Agreement Merging

    Oct 24, 2026Fatema Siddika, Abhijit Chunduru, Zilinghan Li +2Continual Learning for LLMsFederated Continual Learning

    Iowa State University, Ames, USA · Argonne National Laboratory, USA · University of Massachusetts Amherst, Amherst, USA

  8. Nested Sparsity for Scalable Pretraining and Continual Learning

    Oct 22, 2026Tianzhe Chu, Druv Pai, Yi MaConditional ComputationSparse Attention

    University of Hong Kong · UC Berkeley

  9. Delta N-gram: Forward-Only Continual Writing into a Language Model's Native N-gram Memory

    Oct 22, 2026Hao WuPersistent Memory for Language ModelsMemory-Augmented Language Models

    Illinois Institute of Technology

  10. Why Constrained Updates Still Forget: A Functional-Path Analysis of Continual SFT

    Oct 21, 2026Qingsen Ma, Dianyun Wang, Jiahong Liu +1Supervised Fine-TuningContinual Learning for LLMs

    The Chinese University of Hong Kong · Beijing University of Posts and Telecommunications

  11. Dynamics of Hidden Learning Structure

    Oct 21, 2026Qinyou WangNeural Network Training DynamicsDynamic Neural Networks

  12. Combating Catastrophic Forgetting in Continual Domain Adaptation via Knowledge Recasting

    Oct 20, 2026Zijie Liu, Zhen Tan, Charles Fleming +1Continual Learning for LLMsDomain Adaptation

    UNC at Chapel Hill · Stevens Institute of Technology · Cisco Research

  13. FisherEdit: Fisher Information Guided Knowledge Editing for Large Language Models

    Oct 19, 2026Yizhou Yang, Jiahong Fu, Zhiwu Wang +6Knowledge EditingLanguage Model Editing

    Xi’an Jiaotong University · Zhejiang University · City University of Hong Kong +2

  14. Continual Test-Time Learning with Evolution Strategies

    Oct 16, 2026Guangya Hao, Yiting Duan, Longyan Tan +5Continual Learning for LLMsContinual Test-Time Adaptation

    University of Cambridge · Western Sydney University · The Alan Turing Institute +1

  15. Towards Continual Learning: Mitigating Forgetting With Subspace Tuning

    Oct 15, 2026Arnav Chavan, Kusha Sareen, Rishabh Tiwari +6Continual Learning for LLMsLLM Fine-Tuning

    UC Berkeley · Mila · McGill University +5

  16. Selection Is Not Learning: Choosing Model Futures

    Oct 15, 2026Haoyi Yang, Kshitij Mishra, Nils Lukas +1Language Model Self-ImprovementSelf-Training for Language Models

    Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE

  17. Functional Subnetwork Localization for Efficient Continual Learning in Large Language Models

    Oct 15, 2026Shuoran Jiang, Wentao Zhu, Yang Xiang +8Continual Learning for LLMsIncremental Learning

    School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China · China Academy of Engineering Physics, Mianyang, China · Peng Cheng Laboratory, Shenzhen, China +2

  18. Learning from Interaction Experience via Localized Context Distillation

    Oct 15, 2026Zhezheng Hao, Yongkang Yang, Xilin Xia +9Context DistillationContinual Learning for LLMs

    Zhejiang University · Workbuddy Team, Tencent · The Chinese University of Hong Kong +1

  19. Ranking and Partitioning of Attention Heads for Sparse and Continual Reinforcement Learning Fine-tuning

    Oct 15, 2026Ruizhe Shi, Zaiwen Yang, Qiubo Wang +2Continual Learning for LLMsSparse Fine-Tuning

    University of Washington · Tsinghua University · University of Hong Kong +1

  20. Open-world AI: a Tale of Three Requirements

    Oct 14, 2026Jianyu Zhang, Léon BottouContinual Learning for LLMs

    FAIR, Meta∗ · Flatiron Institute

  21. Cortex: Consolidating Context into Parametric Memory

    Oct 14, 2026Jintian Zhang, Feng He, Xinnian Liang +5Continual Learning for LLMsKnowledge Editing

    Zhejiang University · ByteDance Seed · Tsinghua University

  22. Making LoRA Identifiable: Orthogonal Alignment for Continual Learning

    Oct 12, 2026Pratik Rakesh Singh, Mohammadi Zaki, Akash Saha +2Orthogonal Procrustes AlignmentContinual LoRA

    Sony Research India

  23. Knowledge Interaction in Hypernetwork-based Continual Learning: Conflict and Composition

    Oct 11, 2026Jiaqi Zheng, Yi Hu, Yadong Mu +1Continual Learning for LLMsKnowledge Conflicts in Language Models

    Peking University · Norizon AI

  24. Representation Drift in Continual Learning: LoRA versus N-gram Memory

    Oct 10, 2026Hao WuContinual Learning for LLMsOnline Continual Learning

    Illinois Institute of Technology

  25. Specialists Learn Faster: Knowledge Organization in Pre-training Shapes Knowledge Update Efficiency

    Oct 9, 2026Sheng Zhao, Yuhao ZhuContinual Learning for LLMsContinued Pretraining

    Department of Computer Science University of Rochester

  26. Continually Post-training Small Models via Synthetic Self-distillation

    Oct 9, 2026Woosung Koh, Hyunsoo Lee, Kyungjae Lee +4LLM Post-TrainingContinual Learning for LLMs

    KAIST AI · Work done during an internship at LG AI Research · LG AI Research

  27. Static Gradient Attribution Underperforms a Density-Matched Random Mask on Loss-Based Forgetting Within LoRA B-Matrix

    Oct 8, 2026Yu Sun, Junwei Zhou, Zuodong Xiang +5Storage-Efficient Continual LearningContinual Learning for LLMs

    University of Pennsylvania · Matter Innovation Inc. · University of California, Davis +5

  28. Self-Preservation in Language Models

    Oct 8, 2026Dongkyu Cho, Rumi Chunara, Ali BehrouzContinual Learning for LLMsReplay-Based Continual Learning

    New York University · Google Research · Cornell University

  29. From a Prompt to Repertoires: Evolving Functional REpertoires Enable LLM Continual Learning

    Oct 8, 2026Fengyuan Liu, Yue Wang, Hangxi Guo +4Continual Learning for LLMsPrompt Learning

    The Chinese University of Hong Kong, Shenzhen · Shanghai AI Laboratory · University of Science and Technology of China +2

  30. Stability-Plasticity Balance via Singular-Vector Selection in LLM Continual Learning

    Oct 8, 2026Lingxiang Wang, Hainan Zhang, Liang Pang +2Continual Learning for LLMsParameter-Efficient Fine-Tuning

    School of Artificial Intelligence, Beihang University · Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing · Institute of Computing Technology, Chinese Academy of Sciences