Meta-Learning

ICLR 2024arXiv:2310.08887

#2

METRA: Scalable Unsupervised RL with Metric-Aware Abstraction

Seohong Park, Oleh Rybkin, Sergey Levine

68

ICLR 2025arXiv:2407.10490

#3

Learning Dynamics of LLM Finetuning

YI REN, Danica Sutherland

61

CVPR 2024arXiv:2404.00851

#4

Prompt Learning via Meta-Regularization

Jinyoung Park, Juyeon Ko, Hyunwoo J. Kim

40

ICLR 2024arXiv:2403.13249

#5

A Unified and General Framework for Continual Learning

Zhenyi Wang, Yan Li, Li Shen et al.

37

CVPR 2024arXiv:2403.02628

#6

Interactive Continual Learning: Fast and Slow Thinking

Biqing Qi, Xinquan Chen, Junqi Gao et al.

35

NeurIPS 2025arXiv:2503.09501

#7

ReMA: Learning to Meta-Think for LLMs with Multi-agent Reinforcement Learning

Ziyu Wan, Yunxiang Li, Xiaoyu Wen et al.

meta-thinkingmulti-agent reinforcement learninglarge language modelsreasoning processes+4

35

AAAI 2025arXiv:2402.05813

#8

Selective Forgetting: Advancing Machine Unlearning Techniques and Evaluation in Language Models

Lingzhi Wang, Xingshan Zeng, Jinsong Guo et al.

33

ICLR 2024arXiv:2310.10971

#9

Context-Aware Meta-Learning

Christopher Fifty, Dennis Duan, Ronald Junkins et al.

24

AAAI 2025arXiv:2408.10682

#10

Towards Robust Knowledge Unlearning: An Adversarial Framework for Assessing and Improving Unlearning Robustness in Large Language Models

Hongbang Yuan, Zhuoran Jin, Pengfei Cao et al.

22

CVPR 2024arXiv:2312.00600

#11

Improving Plasticity in Online Continual Learning via Collaborative Learning

Maorong Wang, Nicolas Michel, Ling Xiao et al.

20

ICLR 2025arXiv:2406.09179

#12

Towards Effective Evaluations and Comparisons for LLM Unlearning Methods

Qizhou Wang, Bo Han, Puning Yang et al.

machine unlearninglarge language modelsevaluation metrics robustnessknowledge retention trade-offs+4

18

ICLR 2025arXiv:2410.06209

#13

A Good Learner can Teach Better: Teacher-Student Collaborative Knowledge Distillation

Ayan Sengupta, Shantanu Dixit, Md Shad Akhtar et al.

OpenUnlearning: Accelerating LLM Unlearning via Unified Benchmarking of Methods and Metrics

Vineeth Dorna, Anmol Mekala, Wenlong Zhao et al.

LeanAgent: Lifelong Learning for Formal Theorem Proving

Adarsh Kumarappan, Mohit Tiwari, Peiyang Song et al.

12

NeurIPS 2025arXiv:2512.24695

#16

Knowledge-Aware Parameter Coaching for Personalized Federated Learning

Mingjian Zhi, Yuanguo Bi, Wenchao Xu et al.

Nested Learning: The Illusion of Deep Learning Architectures

Ali Behrouz, Meisam Razaviyayn, Peilin Zhong et al.

nested learning paradigmin-context learningexpressive optimizersassociative memory modules+4

9

NeurIPS 2025arXiv:2505.22453

#18

First SFT, Second RL, Third UPT: Continual Improving Multi-Modal LLM Reasoning via Unsupervised Post-Training

Lai Wei, Yuting Li, Chen Wang et al.

9

ICLR 2025arXiv:2412.16577

#19

A Meta-Learning Approach to Bayesian Causal Discovery

Anish Dhir, Matthew Ashman, James Requeima et al.

8

AAAI 2025arXiv:2412.15571

#20

Continual Learning Using a Kernel-Based Method Over Foundation Models

Saleh Momeni, Sahisnu Mazumder, Bing Liu

7

NeurIPS 2025arXiv:2505.17895

#21

DataRater: Meta-Learned Dataset Curation

Dan Andrei Calian, Greg Farquhar, Iurii Kemaev et al.

6

NeurIPS 2025arXiv:2505.18943

#22

MetaMind: Modeling Human Social Thoughts with Metacognitive Multi-Agent Systems

Xuanming Zhang, Yuxuan Chen, Samuel (Min-Hsuan) Yeh et al.

theory of mindmulti-agent systemssocial reasoningmetacognitive modeling+4

6

NeurIPS 2025arXiv:2505.09666

#23

System Prompt Optimization with Meta-Learning

Yumin Choi, Jinheon Baek, Sung Ju Hwang

system prompt optimizationmeta-learning frameworkbilevel optimizationprompt transferability+4

NeurIPS 2025arXiv:2505.17010

#24

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving

Jin Zhang, Flood Sung, Zhilin Yang et al.

Learning to Unlearn while Retaining: Combating Gradient Conflicts in Machine Unlearning

Gaurav Patel, Qiang Qiu

Understanding Prompt Tuning and In-Context Learning via Meta-Learning

Tim Genewein, Kevin Li, Jordi Grau-Moya et al.

prompt tuningin-context learningmeta-learningbayesian predictors+4

ICLR 2024arXiv:2212.01168

#27

Towards Cross Domain Generalization of Hamiltonian Representation via Meta Learning

Yeongwoo Song, Hawoong Jeong

ICML 2025arXiv:2502.07709

#28

MAGELLAN: Metacognitive predictions of learning progress guide autotelic LLM agents in large goal spaces

Loris Gaven, Thomas Carta, Clément Romac et al.

ECCV 2024arXiv:2407.08411

#29

CLEO: Continual Learning of Evolving Ontologies

Shishir Muralidhara, Saqib Bukhari, Georg Dr. Schneider et al.

3

AAAI 2025arXiv:2412.13790

#30

Toward Efficient Data-Free Unlearning

Chenhao Zhang, Shaofei Shen, Weitong Chen et al.

3

AAAI 2024arXiv:2403.03082

#31

Recall-Oriented Continual Learning with Generative Adversarial Meta-Model

Haneol Kang, Dong-Wan Choi

continual learningstability-plasticity dilemmagenerative adversarial meta-modelrecall-oriented framework+3

3

AAAI 2024arXiv:2401.05097

#32

Metalic: Meta-Learning In-Context with Protein Language Models

Jacob Beck, Shikha Surana, Manus McAuliffe et al.

Any-Way Meta Learning

JunHoo Lee, Yearim Kim, Hyunho Lee et al.

meta learninglabel equivalenceany-way learningdomain generalization+4

2

ICML 2025arXiv:2212.14681

#34

An Entropy-Based Model for Hierarchical Learning

Amir R. Asadi

2

ICML 2025arXiv:2505.00793

#35

Scalable Meta-Learning via Mixed-Mode Differentiation

Iurii Kemaev, Dan Andrei Calian, Luisa Zintgraf et al.

2

ICML 2025arXiv:2506.00453

#36

TMetaNet: Topological Meta-Learning Framework for Dynamic Link Prediction

Hao Li, Hao Wan, Yuzhou Chen et al.

AAAI 2024arXiv:2301.02761

#37

Active Learning Guided by Efficient Surrogate Learners

Yunpyo An, Suyeong Park, Kwang In Kim

active learningbatch-based samplinggaussian process surrogatesurrogate learners+3

AAAI 2025arXiv:2410.10417

#38

A Stochastic Approach to Bi-Level Optimization for Hyperparameter Optimization and Meta Learning

Minyoung Kim, Timothy Hospedales

ECCV 2024arXiv:2311.02879

#39

Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling

Wonho Bae, Jing Wang, Danica J. Sutherland

active learningmeta-learningcontext set labelinggaussian mixture models+1

CVPR 2025arXiv:2503.12053

#40

Ferret: An Efficient Online Continual Learning Framework under Varying Memory Constraints

Yuhao Zhou, Yuxin Tian, Jindi Lv et al.

NeurIPS 2025arXiv:2601.19055

#41

Towards Cost-Effective Learning: A Synergy of Semi-Supervised and Active Learning

Tianxiang Yin, Ningzhong Liu, Han Sun

Meta-Learning Hyperparameters for Parameter Efficient Fine-Tuning

Zichen Tian, Yaoyao Liu, Qianru Sun

Meta-Learning Dynamic Center Distance: Hard Sample Mining for Learning with Noisy Labels

Chenyu Mu, Yijun Qu, Jiexi Yan et al.

From Dormant to Deleted: Tamper-Resistant Unlearning Through Weight-Space Regularization

Shoaib Ahmed Siddiqui, Adrian Weller, David Krueger et al.

Privacy-Aware Lifelong Learning

Ozan Özdenizci, Elmar Rueckert, Robert Legenstein

Principled Fine-tuning of LLMs from User-Edits: A Medley of Preference, Supervision, and Reward

Dipendra Misra, Aldo Pacchiano, Ta-Chung Chi et al.

preference learningsupervised fine-tuningreward modelinguser-edit data+3

ECCV 2024arXiv:2410.11878

#47

Neural Metamorphosis

Xingyi Yang, Xinchao Wang

neural implicit functionsweight manifold learninghypernetworksmodel compression+3

NeurIPS 2025arXiv:2506.00359

#48

Keeping an Eye on LLM Unlearning: The Hidden Risk and Remedy

Jie Ren, Zhenwei Dai, Xianfeng Tang et al.

large language modelsmachine unlearningadversarial attacksmodel utility degradation+3

#49

Meta-D2AG: Causal Graph Learning with Interventional Dynamic Data

Tian Gao, Songtao Lu, Junkyu Lee et al.

causal graph learningdynamic dag discoverynon-stationary time seriesdistribution shift+4

NeurIPS 2025arXiv:2506.05735

#50

Do LLMs Really Forget? Evaluating Unlearning with Knowledge Correlation and Confidence Awareness

Rongzhe Wei, Peizhi Niu, Hans Hao-Hsun Hsu et al.

machine unlearningknowledge graphslarge language modelsinferential dependencies+3

#51

LOIRE: LifelOng learning on Incremental data via pre-trained language model gRowth Efficiently

Xue Han, Yitong Wang, Junlan Feng et al.

Provably Efficient Multi-Task Meta Bandit Learning via Shared Representations

Jiabin Lin, Shana Moothedath

multi-task bandit learningrepresentation learninglinear banditsmeta-learning+3

#53

Continual Slow-and-Fast Adaptation of Latent Neural Dynamics (CoSFan): Meta-Learning What-How & When to Adapt

Ryan Missel, Linwei Wang

Modelling the control of offline processing with reinforcement learning

Eleanor Spens, Neil Burgess, Tim Behrens

memory replayoffline learningreinforcement learningmeta-controller+4

ICLR 2025arXiv:2405.16124

#55

On the Stability and Generalization of Meta-Learning: the Impact of Inner-Levels

Wenjun Ding, Jingling Liu, Lixing Chen et al.

Better than Your Teacher: LLM Agents that learn from Privileged AI Feedback

Sanjiban Choudhury, Paloma Sodhi

Unsupervised Meta-Learning via In-Context Learning

Anna Vettoruzzo, Lorenzo Braccaioli, Joaquin Vanschoren et al.

unsupervised meta-learningin-context learningtransformer architecturessequence modeling+4

NeurIPS 2025arXiv:2410.22264

#58

Towards Multi-Table Learning: A Novel Paradigm for Complementarity Quantification and Integration

Zhang Junyu, Lizhong Ding, MinghongZhang et al.

Provable Meta-Learning with Low-Rank Adaptations

Jacob Block, Sundararajan Srinivasan, Liam Collins et al.

foundation modelsparameter-efficient fine-tuningmeta-learninglow-rank adaptation+3

#60

LLMOPT: Learning to Define and Solve General Optimization Problems from Scratch

caigao jiang, Xiang Shu, Hong Qian et al.

Learning to Think: Information-Theoretic Reinforcement Fine-Tuning for LLMs

Jingyao Wang, Wenwen Qiang, Zeen Song et al.

Flexible inference for animal learning rules using neural networks

Yuhan Helena Liu, Victor Geadah, Jonathan Pillow

Machine Unlearning via Simulated Oracle Matching

Kristian G Georgiev, Roy Rinberg, Sam Park et al.

machine unlearningdata attributionforget set removalmodel fine-tuning+3

#64

LiFT: Learning to Fine-Tune via Bayesian Parameter Efficient Meta Fine-Tuning

Minyoung Kim, Timothy Hospedales

parameter-efficient fine-tuningmeta-learninghierarchical bayesian modellora modules+3

#65

Learning to Learn with Contrastive Meta-Objective

Shiguang Wu, Yaqing Wang, Yatao Bian et al.

Why In-Context Learning Models are Good Few-Shot Learners?

Shiguang Wu, Yaqing Wang, Quanming Yao

in-context learningfew-shot learningmeta-learningtransformer architecture+4

NeurIPS 2025arXiv:2507.01649

#67

GradMetaNet: An Equivariant Architecture for Learning on Gradients

Yoav Gelberg, Yam Eitan, Aviv Navon et al.

equivariant architecturegradient processinglearned optimizationneuron permutation symmetry+4

#68

Learn and Ensemble Bridge Adapters for Multi-domain Task Incremental Learning

Ziqi Gu, Chunyan Xu, Wenxuan Fang et al.

multi-domain incremental learningschrödinger bridge adaptationadapter ensemble strategycatastrophic forgetting mitigation+4

NeurIPS 2025arXiv:2510.19797

#69

Wisdom is Knowing What not to Say: Hallucination-Free LLMs Unlearning via Attention Shifting

Chenchen Tan, Youyang Qu, Xinghao Li et al.

When Does Curriculum Learning Help? A Theoretical Perspective

Raman Arora, Yunjuan Wang, Kaibo Zhang

Transformers are almost optimal metalearners for linear classification

Roey Magen, Gal Vardi

in-context learningmetalearninglinear classificationtransformer architecture+3

NeurIPS 2025arXiv:2506.05252

#72

Conservative classifiers do consistently well with improving agents: characterizing statistical and online learning

Dravyansh Sharma, Alec Sun

strategic classificationonline learningimprovement regionsproper learning+4

NeurIPS 2025arXiv:2505.23946

#73

Evolving and Regularizing Meta-Environment Learner for Fine-Grained Few-Shot Class-Incremental Learning

Li-Jun Zhao, Zhen-Duo Chen, Yongxin Wang et al.

Learning Expandable and Adaptable Representations for Continual Learning

Ruilong Yu, Mingyan Liu, Fei Ye et al.

Federated Continual Learning via Orchestrating Multi-Scale Expertise

Xiaoyang Yi, Yang Liu, Binhan Yang et al.

Mentored Learning: Improving Generalization and Convergence of Student Learner

Xiaofeng Cao, Yaming Guo, Heng Tao Shen et al.

Lessons Learned: A Multi-Agent Framework for Code LLMs to Learn and Improve

Yuanzhe Liu, Ryan Deng, Tim Kaler et al.

multi-agent systemscode optimizationllm collaborationlesson learning+3

NeurIPS 2025arXiv:2510.22039

#78

Towards Automated Knowledge Integration From Human-Interpretable Representations

Katarzyna Kobalczyk, Mihaela van der Schaar

Unlearning or Obfuscating? Jogging the Memory of Unlearned LLMs via Benign Relearning

Shengyuan Hu, Yiwei Fu, Steven Wu et al.

Predictive Coding Enhances Meta-RL To Achieve Interpretable Bayes-Optimal Belief Representation Under Partial Observability

Po-Chen Kuo, Han Hou, Will Dabney et al.

predictive codingmeta reinforcement learningpartial observabilitybelief state representation+4

ICLR 2025arXiv:2412.08021

#81

Probing Hidden Knowledge Holes in Unlearned LLMs

Myeongseob Ko, Hoang Anh Just, Charles Fleming et al.

Chain-of-Thought Provably Enables Learning the (Otherwise) Unlearnable

Chenxiao Yang, Zhiyuan Li, David Wipf

Can a MISL Fly? Analysis and Ingredients for Mutual Information Skill Learning

Chongyi Zheng, Jens Tuyls, Joanne Peng et al.

mutual information skill learningcontrastive representation learningsuccessor featuresself-supervised learning+3

#84

Learning-Augmented Algorithms for $k$-median via Online Learning

Anish Hebbar, Rong Ge, Amit Kumar et al.

learning-augmented algorithmsonline learningk-median clusteringalgorithmic performance+1

#85

Uncertainty-Informed Meta Pseudo Labeling for Surrogate Modeling with Limited Labeled Data

Xingyu Ren, Pengwei Liu, Pengkai Wang et al.

surrogate modelingneural operatorssemi-supervised learningpseudo-label quality+4

NeurIPS 2025arXiv:2506.13464

#86

LILO: Learning to Reason at the Frontier of Learnability

Thomas Foster, Anya Sims, Johannes Forkel et al.

Unveiling the Learning Mind of Language Models: A Cognitive Framework and Empirical Study

Zhengyu Hu, Jianxun Lian, Zheyuan Xiao et al.

language model learningcognitive psychology frameworkknowledge acquisitionconceptual generalization+3

NeurIPS 2025arXiv:2510.23013

#88

MoEMeta: Mixture-of-Experts Meta Learning for Few-Shot Relational Learning

Han Wu, Jie Yin

few-shot learningknowledge graph reasoningrelational learningmixture-of-experts models+4

NeurIPS 2025arXiv:2503.18403

#89

Knowledge Graph Enhanced Generative Multi-modal Models for Class-Incremental Learning

Xusheng Cao, Haori Lu, Linlan Huang et al.

#90

Semantic Aware Representation Learning for Lifelong Learning

Fahad Sarfraz, Elahe Arani, Bahram Zonooz

lifelong learningsemantic representation learningsparse activation codesknowledge consolidation+4

ICCV 2025arXiv:2410.12777

#91

Learning to Unlearn for Robust Machine Unlearning

Mark HUANG, Lin Geng Foo, Jun Liu

Meta-Unlearning on Diffusion Models: Preventing Relearning Unlearned Concepts

Hongcheng Gao, Tianyu Pang, Chao Du et al.

diffusion modelsconcept unlearningmalicious finetuningmodel misuse prevention+3

NeurIPS 2025arXiv:2510.10365

#93

Is Meta-Learning Out? Rethinking Unsupervised Few-Shot Classification with Limited Entropy

Yunchuan Guan, Yu Liu, Ke Zhou et al.

MetaMorph: Multimodal Understanding and Generation via Instruction Tuning

Shengbang Tong, David Fan, Jiachen Zhu et al.

PointMAC: Meta-Learned Adaptation for Robust Test-Time Point Cloud Completion

Linlian Jiang, Rui Ma, Li Gu et al.

point cloud completiontest-time adaptationmeta-auxiliary learningself-supervised refinement+3

ICCV 2025arXiv:2508.02293

#96

Towards Real Unsupervised Anomaly Detection Via Confident Meta-Learning

Muhammad Aqeel, Shakiba Sharifi, Marco Cristani et al.

CVPR 2025arXiv:2503.18987

#97

Balanced Direction from Multifarious Choices: Arithmetic Meta-Learning for Domain Generalization

Xiran Wang, Jian Zhang, Lei Qi et al.

domain generalizationdistribution shiftmeta-learning algorithmsgradient matching theory+3

ICCV 2025arXiv:2508.06301

#98

MagMax: Leveraging Model Merging for Seamless Continual Learning

Daniel Marczak, Bartlomiej Twardowski, Tomasz Trzcinski et al.

FedMeNF: Privacy-Preserving Federated Meta-Learning for Neural Fields

Junhyeog Yun, Minui Hong, Gunhee Kim

#100

Learning on the Go: A Meta-learning Object Navigation Model

Xiaorong Qin, Xinhang Song, Sixian Zhang et al.

ICCV 2025