Replay-Based Continual Learning

Follow this topic Ask about this topic

Momentum

9 papers in the last four weeks, up 125% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 82

All topics

RECALL: Recovery Experience Collection for Active Lifelong Learning in Vision-Language-Action Models
Jun 22, 2026Ulas Berk Karli, Tesca FitzgeraldContinual Robot Learning Robot Skill Learning
Department of Computer Science Yale University United States
C^2GR: Coupled Comprehensive Generative Replay for a Continually Learnable Universal Segmentation Model
Jun 22, 2026Wei Li, Jingyang Zhang, Guoan Wang +4Cross-Modality Medical Image Segmentation Replay-Based Continual Learning
Shanghai Jiao Tong University · Southeast University · Stevens Institute of Technology +2
Domain-incremental audio classification using domain-specific experts and prototype classifier
Jun 22, 2026Jongyeon Park, Do-Hyeon Lim, Sang-won Park +4Generative Replay Domain-Incremental Learning
Dept. of AI Convergence, Gwangju Institute of Science and Technology, Gwangju 61005, Korea · Dept. of EECS, Gwangju Institute of Science and Technology, Gwangju 61005, Korea · AI Lab., R&D Center, Hanwha Vision, Seongnam-si, Gyeonggi-do 13488, Korea
PreAct: Computer-Using Agents that Get Faster on Repeated Tasks
Jun 16, 2026Bojie LiComputer-Use Agents LLM Agent Skill Learning
Pine AI
Continual Online Personalization of Exoskeleton Control via Manifold-Aware Experience Replay
Jun 16, 2026Changseob Song, Inseung KangRobotic Control Ankle Exoskeleton Control
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
When Generator Replay Degrades: Projected Rehearsal Orchestration for Heterogeneous Federated Class-Incremental Learning
Jun 14, 2026Thinh T. H. Nguyen, Khoa D. Doan, Binh T. Nguyen +2Class-Incremental Learning Federated Continual Learning
VinUniversity, Hanoi, Vietnam · VNU-HCM, University of Science, Ho Chi Minh City, Vietnam · Technische Universität Berlin, Germany
Amnesia: A Stealthy Replay Attack on Continual Learning Dreams
Jun 10, 2026Ahmed Sharshar, Naveen Kumar Kummari, Mohsen GuizaniReplay-Based Continual Learning
Department of Computer Vision Mohamed bin Zayed University of Artificial Intelligence, AbuDhabi, UAE · Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence, AbuDhabi, UAE · Department of Machine Learning2026 Mohamed bin Zayed University of Artificial Intelligence, AbuDhabi, UAE
Theoretical Foundations of Continual Learning via Drift-Plus-Penalty
Jun 7, 2026Nazreen Shah, Govinda Arya, Bharath B. N. +1Continual Learning Replay-Based Continual Learning
IIIT Delhi · IIT Dharwad
Not Just After One: Sleep-Inspired Replay Prevents Catastrophic Forgetting After Sequential Tasks
Jun 7, 2026Anthony Bazhenov, Jean Erik Delanois, Giri P. KrishnanReplay-Based Continual Learning Memory Consolidation
Khoury College of Computer Sciences, Northeastern University, Boston, MA · Department of Medicine, University of California, San Diego, La Jolla, CA, USA · ARTISAN, Georgia Institute of Technology, Atlanta, GA USA
CLaaS: Continual learning as a service for sample efficient online learning
Jun 4, 2026Kion Fallah, Silen Naihin, Barak Widawsky +1Continual Learning for LLM Agents Continual Learning
1Resolute Labs · 2South Park Commons · 3Incept Labs
Continual Visual and Verbal Learning Through a Child's Egocentric Input
Jun 3, 2026Xiaoyang Jiang, Yanlai Yang, Kenneth A. Norman +2Egocentric Video Understanding Replay-Based Continual Learning
Agentic Learning AI Lab, New York University · Department of Psychology, Princeton University
Language Models Need Sleep: Learning to Self-Modify and Consolidate Memories
Jun 2, 2026Ali Behrouz, Farnoosh Hashemi, Adel Javanmard +1Continual Learning for LLMs Persistent Memory for Language Models
Google
FlashbackCL: Mitigating Temporal Forgetting in Federated Learning
Jun 2, 2026Mubarak A. Ojewale, Adriana E. Chis, Jorge M. Cortes-Mendoza +2Distribution Shift Federated Continual Learning
Cloud Competency Centre, National College of Ireland, Dublin, Ireland
PHASER: Phase-Aware and Semantic Experience Replay for Vision-Language-Action Models
Jun 2, 2026Ziyang Chen, Shaoguang Wang, Weiyu Guo +5Continual Learning for VLA Models Robotic Manipulation
Thrust of AI, HKUST(Guangzhou), Guangzhou, China · AI2 Robotics, Shenzhen, China
Combining Statistical Features and Deep Encodings for Rehearsal-Based Class-Incremental Time Series Classification
Jun 2, 2026Pablo García-Santaclara, Bruno Fernández-Castro, Rebeca Pilar Díaz-RedondoClass-Incremental Learning Multivariate Time Series Classification
atlanTTic – ICLAB, Universidade de Vigo, Vigo 36310, Spain · Centro Tecnolóxico de Telecomunicacións de Galicia (GRADIANT), Vigo 36214, Spain
BBR-Net: Boundary-Balanced Replay for Continual Medical Image Segmentation
Jun 2, 2026Zahid Ullah, Sieun Choi, Jihie KimInteractive Medical Image Segmentation Ultrasound Image Segmentation
Department of Computer Science and Artificial Intelligence, Dongguk University, Seoul, 04620, Republic of Korea
Learning to Solve, Forgetting to Retain: Correct-Set Turnover in RLVR
Jun 2, 2026Chuanyu Qin, Chenxu Yang, Qingyi Si +3Continual Learning for LLMs Reinforcement Learning
Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China · School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China
EvoBrain: Continual Learning of EEG Foundation Models Across Heterogeneous BCI Tasks
Jun 1, 2026Yangxuan Zhou, Sha Zhao, Jiquan Wang +2Brain-Computer Interfaces EEG Decoding
State Key Laboratory of Brain-machine Intelligence and College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China · MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University, Hangzhou, Zhejiang, China
SHARP: Sleep-based Hierarchical Accelerated Replay for Long Range Non-Stationary Temporal Pattern Recognition
May 30, 2026Jayanta Dey, Shikhar Srivastava, Itamar Lerner +2Memory-Augmented Neural Networks Continual Learning
University of Texas at San Antonio, USA · University of Rochester, USA
Canonicalized Stable-List Replay for Private Federated Continual Learning over Language-Model Embeddings
May 29, 2026Ibne Farabi Shihab, Abu Sa-Adat Mohamed Moon-Im Al Ahsan, Anuj SharmaFederated Continual Learning Replay-Based Continual Learning
Department of Computer Science, Iowa State University · Department of Computer Science & Engineering, BRAC University · Department of Civil, Construction & Environmental Engineering, Iowa State University
Dynamic Proxy-Mixing: Transferring Replay Controllers from Small to Large Models for Continual Instruction Tuning
May 29, 2026Ibne Farabi Shihab, Fariya Afrin, Anuj SharmaContinual Learning for LLMs Replay-Based Continual Learning
Department of Computer Science, Iowa State University · Department of Computer Science, Kalinga Institute of Industrial Technology · Department of Civil, Construction & Environmental Engineering, Iowa State University
On-Policy Replay for Continual Supervised Fine-Tuning
May 28, 2026Yan Chen, Taojie Zhu, Meng Zhang +4Continual Learning for LLMs Replay-Based Continual Learning
Tsinghua University · Alibaba Group
Continual Learning in Modern Hopfield Networks with an Application to Diffusion Models
May 27, 2026Ken Takeda, Masafumi Oizumi, Ryo KarakidaHopfield Networks Replay-Based Continual Learning
Graduate School of Arts and Science, The University of Tokyo, Tokyo, Japan · Artificial Intelligence Research Center, AIST, Tokyo, Japan
Can VLA Models Learn from Real-World Data Continually without Forgetting?
May 26, 2026Jiarun Zhu, Yijun Hong, Xiaoquan Sun +5Continual Robot Learning Continual Learning for VLA Models
1HKU · 5SUSTech · 2INFIFORCE +3
Forgetting in Language Models: Capacity, Optimization, and Self-Generated Replay
May 25, 2026Martin Marek, Dongkyu Cho, Shikai Qiu +3Continual Learning for LLMs LLM Fine-Tuning
New York University
Don't Forget the Critic: Value-Based Data Rehearsal for Multi-Cyclic Continual Reinforcement Learning
May 21, 2026Benjamin Poole, Andrew Quinn, Li Yang +1Reinforcement Learning Deep Q-Learning
Department of Computer Science University of North Carolina at Charlotte
MANGO: Meta-Adaptive Network Gradient Optimization for Online Continual Learning
May 18, 2026Ankita Awasthi, Marco Apolinario, Kaushik RoyClass-Incremental Learning Meta-Learning
Purdue University, USA · TU Delft, Netherlands
Domain Incremental Learning for Pandemic-Resilient Chest X-Ray Analysis
May 18, 2026Danu KimChest X-Ray Classification Medical Diagnosis
Korea International School, Jeju Campus, Jeju-do 63644, Republic of Korea
LAPS: Improving Incremental LiDAR Mapping using Active Pooling and Sampling for Neural Distance Fields
May 15, 2026Dongjae Lee, Wooseong Yang, Yifu Tao +2Simultaneous Localization and Mapping Replay-Based Continual Learning
Department of Mechanical Engineering, Seoul National University, S. Korea · Oxford Robotics Institute at the University of Oxford, UK
Understanding Imbalanced Forgetting in Rehearsal-Based Class-Incremental Learning
May 14, 2026Alberto Tamajo, Srinandan Dasmahapatra, Rahman AttarClass-Incremental Learning Gradient Interference
School of Electronics and Computer Science, University of Southampton, SO17 1BJ Southampton, United Kingdom

Jun 22, 2026, cs.ROJ/K move · Enter open · S save

RECALL: Recovery Experience Collection for Active Lifelong Learning in Vision-Language-Action Models

Ulas Berk Karli, Tesca Fitzgerald

Department of Computer Science Yale University United States

Continual Robot Learning Robot Skill Learning Elastic Weight Consolidation Imitation Learning Continual Learning for VLA Models Data-Efficient Robot Learning+3

Open paper AskPDF arXiv

Vision-Language-Action (VLA) models are commonly fine-tuned through passive imitation learning, where additional demonstrations are collected for tasks where the policy performs poorly. This approach incurs several downsides: it requires the robot to fail before data collection is triggered, provides little guidance about which states require supervision, and wastes demonstrator effort on redundant parts of the task where the policy already performs well. In this paper, we propose an active, continual learning paradigm for VLAs. We demonstrate that active, uncertainty-guided data collection leads to more efficient fine-tuning than when using passively-collected demonstrations. However, we also find that fine-tuning only on actively-collected recovery data leads to catastrophic forgetting. We evaluate techniques for continual learning, including replay-based data mixing and elastic weight consolidation, and identify tradeoffs between plasticity to uncertainty-guided recovery data and retention of previously learned behaviors. Overall, our work contributes an empirical study of active continual learning for autoregressive VLAs, establishing that uncertainty-guided recovery demonstrations can improve adaptation efficiency while also revealing open challenges when targeted new data is incorporated into large robot policies.

Momentum

Related topics

Latest papers 82