Continual Learning for LLMs

LLM: Large Language Model

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21 papers in the last four weeks, up 250% on the four weeks before. 0.2% of all new papers.

Jul 13Week of Sep 28

Latest papers 151

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Skill Neologisms: Towards Skill-based Continual Learning
May 6, 2026Antonin Berthon, Nicolas Astorga, Mihaela van der SchaarContinual Learning for LLMs
University of Cambridge
Stabilizing LLM Supervised Fine-Tuning via Explicit Distributional Control
May 6, 2026Xinyu Wang, Changzhi Sun, Yuanbin Wu +1Supervised Fine-Tuning Continual Learning for LLMs
School of Computer Science and Technology, East China Normal University, Shanghai, China · TeleAI, Shanghai, China
Memory as a Markov Matrix: Sample Efficient Knowledge Expansion via Token-to-Dictionary Mapping
May 5, 2026Kaustubh Pethkar, Ziyang Xiong, Zuofeng Shang +1Continual Learning for LLMs Memorization in Language Models
New Jersey Institute of Technology · University of California, Berkeley
CASCADE: Case-Based Continual Adaptation for Large Language Models During Deployment
May 5, 2026Siyuan Guo, Yali Du, Hechang Chen +2Continual Learning for LLM Agents Continual Learning for LLMs
aSchool of Artificial Intelligence, Jilin University. · bEngineering Research Center of Knowledge-Driven Human-Machine Intelligence, Jilin University. · cInternational Center of Future Science, Jilin University. +3
Sentinel-VLA: A Metacognitive VLA Model with Active Status Monitoring for Dynamic Reasoning and Error Recovery
May 2, 2026Wenhao Li, Xiu Su, Dan Niu +6Continual Learning for LLMs Robotics
University of Sydney · Central South University · Southeast University +2
Continual Calibration: Coverage Can Collapse Before Accuracy in Lifelong LLM Fine-Tuning
Apr 27, 2026Ibne Farabi Shihab, Sanjeda Akter, Anuj SharmaContinual Learning for LLMs Conformal Prediction
Department of Computer Science, Iowa State University · Department of Civil, Construction & Environmental Engineering, Iowa State University
OptProver: Bridging Olympiad and Optimization through Continual Training in Formal Theorem Proving
Apr 26, 2026Chenyi Li, Yanchen Nie, Zhenyu Ming +3Continual Learning for LLMs Automated Theorem Proving
School of Mathematical Sciences, Peking University · 2Huawei Technologies · Center for Machine Learning Research, Peking University +1
Hidden Failure Modes of Gradient Modification under Adam in Continual Learning, and Adaptive Decoupled Moment Routing as a Repair
Apr 24, 2026Yuelin Hu, Zhenbo Yu, Zhengxue Cheng +2Continual Learning for LLMs Adaptive Gradient Methods
Shanghai Jiao Tong University · Shanghai Maritime University
COMPASS: COntinual Multilingual PEFT with Adaptive Semantic Sampling
Apr 22, 2026Noah FlynnMultilingual Language Models Continual Learning for LLMs
UC Berkeley
Towards Scalable Lifelong Knowledge Editing with Selective Knowledge Suppression
Apr 21, 2026Dahyun Jung, Jaewook Lee, Heuiseok LimKnowledge Editing Continual Learning for LLMs
Department of Computer Science and Engineering, Korea University
Train Separately, Merge Together: Modular Post-Training with Mixture-of-Experts
Apr 20, 2026Jacob Morrison, Sanjay Adhikesaven, Akshita Bhagia +3Continual Learning for LLMs Mixture-of-Experts Language Models
University of Washington · Allen Institute for AI · University of California, Berkeley
HiP-LoRA: Budgeted Spectral Plasticity for Robust Low-Rank Adaptation
Apr 20, 2026Lixian Chen, Jianhong TanContinual Learning for LLMs Low-Rank Adaptation
Guangdong University of Technology
SafeAnchor: Preventing Cumulative Safety Erosion in Continual Domain Adaptation of Large Language Models
Apr 20, 2026Dongxin Guo, Jikun Wu, Siu Ming YiuContinual Learning for LLMs LLM Fine-Tuning
The University of Hong Kong, Hong Kong, China · Brain Investing Limited, Hong Kong, China
Beyond Meta-Reasoning: Metacognitive Consolidation for Self-Improving LLM Reasoning
Apr 19, 2026Ziqing Zhuang, Linhai Zhang, Jiasheng Si +2Continual Learning for LLMs Meta-Learning
Southeast University · King’s College London · Qilu University of Technology (Shandong Academy of Sciences) +1
Continual Safety Alignment via Gradient-Based Sample Selection
Apr 19, 2026Thong Bach, Dung Nguyen, Thao Minh Le +1Continual Learning for LLMs LLM Fine-Tuning
Applied Artificial Intelligence Initiative (A2I2), Deakin University · Pennsylvania State University
Efficient Task Adaptation in Large Language Models via Selective Parameter Optimization
Apr 18, 2026Weijie Wan, Jiangjiang ZhaoContinual Learning for LLMs LLM Fine-Tuning
Shortest Path Technology · China Mobile Communications Group Co.,Ltd
JumpLoRA: Sparse Adapters for Continual Learning in Large Language Models
Apr 17, 2026Alexandra Dragomir, Ioana Pintilie, Antonio Barbalau +6Continual Learning for LLMs Low-Rank Adaptation
Bitdefender · ETH Zurich · University of Bucharest
Why Fine-Tuning Encourages Hallucinations and How to Fix It
Apr 16, 2026Guy Kaplan, Zorik Gekhman, Zhen Zhu +5Supervised Fine-Tuning Continual Learning for LLMs
♡Hebrew University of Jerusalem · ♣Technion – Israel Institute of Technology · ♢University of Illinois Urbana-Champaign +1
Discovering Novel LLM Experts via Task-Capability Coevolution
Apr 16, 2026Andrew Dai, Boris Meinardus, Ciaran Regan +2Continual Learning for LLMs
Sakana AI
Online Experiential Learning for Language Models
Mar 17, 2026Tianzhu Ye, Li Dong, Qingxiu Dong +3Continual Learning for LLMs Language Model Self-Improvement
Microsoft Research
Understanding LoRA as Knowledge Memory: An Empirical Analysis
Mar 1, 2026Seungju Back, Dongwoo Lee, Naun Kang +4Continual Learning for LLMs Memory-Augmented Language Models
1KAIST · 2Samsung SDS · 3New York University
SPRInG: Continual LLM Personalization via Selective Parametric Adaptation and Retrieval-Interpolated Generation
Jan 15, 2026Seoyeon Kim, Jaehyung KimContinual Learning for LLMs LLM Personalization
Yonsei University
When Less is More: 8-bit Quantization Improves Continual Learning in Large Language Models
Dec 22, 2025Michael S. Zhang, Rishi A. Ruia, Arnav Kewalram +3LLM Quantization Continual Learning for LLMs
Algoverse
Representation Interventions Enable Lifelong Knowledge Memory Control in LLMs
Nov 25, 2025Xuyuan Liu, Shengyu Chen, Xinshuai Dong +6Knowledge Editing Continual Learning for LLMs
Dartmouth College · NEC Laboratories America · Carnegie Mellon University
Parameter Importance-Driven Continual Learning for Foundation Models
Nov 19, 2025Lingxiang Wang, Hainan Zhang, Zhiming ZhengContinual Learning for LLMs Parameter-Efficient Fine-Tuning
Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University · School of Artificial Intelligence, Beihang University
Mixtures of SubExperts for Large Language Continual Learning
Nov 9, 2025Haeyong Kang, Hee Suk Yoon, Dahua Feng +1Continual Learning for LLMs Mixture-of-Experts Language Models
Korea Advanced Institute of Science and Technology (KAIST)
Retaining by Doing: The Role of On-Policy Data in Mitigating Forgetting
Oct 21, 2025Howard Chen, Noam Razin, Karthik Narasimhan +1Continual Learning for LLMs RL Post-Training
Princeton Language and Intelligence, Princeton University.
K-Merge: Online Continual Merging of Adapters for On-device Large Language Models
Oct 15, 2025Donald Shenaj, Ondrej Bohdal, Taha Ceritli +3Continual Learning for LLMs On-Device Language Model Inference
Samsung R&D Institute UK · University of Pisa · University of Padova
Reinforcement Fine-Tuning Naturally Mitigates Forgetting in Continual Post-Training
Jul 7, 2025Song Lai, Haohan Zhao, Rong Feng +9Continual Learning for LLMs LLM Post-Training
Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, Hong Kong, China · City University of Hong Kong, Hong Kong, China · School of Artificial Intelligence, Jilin University, Changchun, China +3
TreeLoRA: Efficient Continual Learning via Layer-Wise LoRAs Guided by a Hierarchical Gradient-Similarity Tree
Jun 12, 2025Yu-Yang Qian, Yuan-Ze Xu, Zhen-Yu Zhang +2Continual Learning for LLMs Low-Rank Adaptation
National Key Laboratory for Novel Software Technology, Nanjing University, China · School of Artificial Intelligence, Nanjing University, China · RIKEN AIP, Tokyo, Japan

May 6, 2026, cs.LGJ/K move · Enter open · S save

Skill Neologisms: Towards Skill-based Continual Learning

Antonin Berthon, Nicolas Astorga, Mihaela van der Schaar

University of Cambridge

Continual Learning for LLMs

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Modern LLMs show mastery over an ever-growing range of skills, as well as the ability to compose them flexibly. However, extending model capabilities to new skills in a scalable manner is an open problem: fine-tuning and parameter-efficient variants risk catastrophic forgetting, while context-based approaches have limited expressiveness and are constrained by the model's effective context. We explore skill neologisms--soft tokens integrated in the model's vocabulary and optimized to improve capabilities over a specific skill--as a way to selectively acquire new skills without weight updates. We first observe that pre-trained LLMs already exhibit tokens associated with procedural knowledge. We then show on a controlled synthetic task that skill neologisms can be learned to improve model capabilities on specific skills while being composable with out-of-distribution skills, and that independently trained skill neologisms can be composed zero-shot. Finally, we validate zero-shot composition of independently learned skill neologisms on the more realistic natural language setting of the Skill-Mix benchmark. These results suggest that skill neologisms may provide a scalable path towards skill-based continual learning.

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