Generative Replay

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2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Generative Replay Mitigates Sample Starvation in Quantum Architecture Search
Sep 12, 2026Akash Kundu, Amit Kumar Jaiswal, Sebastian Feld +1Generative Replay Deep Q-Learning
Delft University of Technology, Delft, The Netherlands. · Quantum Computing Division, QuTech, Delft, The Netherlands. · Indian Institute of Technology (BHU), Varanasi, India. +1
A Trust-Network-Based Federated Learning Framework for Multi-Center Aging Clock Prediction
Sep 9, 2026Chunxu Zhang, Bo Li, Wenliang Wang +7Generative Replay Federated Continual Learning
PolyU Academy for Artificial Intelligence, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China. · Quantum Life, Hong Kong, China. · Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China. +3
The World Model Remembers, the Actor Forgets: Dream Rehearsal for Continual Model-Based RL
Jul 22, 2026Gurp NijjerGenerative Replay Reinforcement Learning
Quantegra Research, Canada
Continual Learning with Elastic Regularization and Synthetic Replay for Federated MLLM Fine-Tuning
Jul 13, 2026Jing Liu, Chenxuanyin Zou, Jiayang Ren +5Multimodal Federated Learning Generative Replay
Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada · College of Future Information Technology, Fudan University, Shanghai 200433, China · Department of Chemical and Biological Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada +3
World Action Models Enable Continual Imitation Learning with Recurrent Generative Replays
Jun 25, 2026Manish Kumar Govind, Dominick Reilly, Smit Patel +2Generative Replay Continual Robot Learning
Department of Computer Science University of North Carolina at Charlotte, United States
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
Bidirectional Tutoring for Developmental Motor Learning in Robots: Co-Developed Interaction Dynamics Support Stable Learning
Jun 18, 2026Rui Fukushima, Jun TaniGenerative Replay Robot Skill Learning
Cognitive Neurorobotics Research Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
Towards Understanding Continual Factual Knowledge Acquisition of Language Models: From Theory to Algorithm
May 11, 2026Haoyu Wang, Yifan Shang, Zhongxiang Sun +3Continual Learning for LLMs Generative Replay
Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China · School of Artificial Intelligence and Data Science, University of International Business and Economics, Beijing, China

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Generative Replay Mitigates Sample Starvation in Quantum Architecture Search

Akash Kundu, Amit Kumar Jaiswal, Sebastian Feld, Prayag Tiwari

Delft University of Technology, Delft, The Netherlands. · Quantum Computing Division, QuTech, Delft, The Netherlands. · Indian Institute of Technology (BHU), Varanasi, India.+1

Generative Replay Deep Q-Learning Replay-Based Continual Learning Quantum State Preparation Quantum RL

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Reinforcement learning (RL) can automate quantum architecture search, but its scalability is limited when useful circuit trajectories become rare in the rapidly expanding search space. Existing replay mechanisms reuse observed transitions; the proposed learned model produces additional predicted one step transitions from real state-action seeds. Here we introduce GenQAS, a tensor network-guided RL framework that combines a fixed matrix product state warm-start with prioritized generative replay. A learned local transition model generates synthetic circuit transitions on demand and mixes them with real experience during Double Deep Q-Network updates. Under a random exploration analysis, near ground state circuits occupy a rapidly shrinking region of the accessible state space. We investigate whether real data anchored synthetic replay can improve the effective training signal in this regime. Across chemical Hamiltonian benchmarks from 6 to 12 qubits, GenQAS improves fixed-budget success probability and identifies compact circuits at competitive energy error. At 12 qubits, it improves final success probability by up to

7.0\times

over passive replay. On a 15-qubit transverse field Ising model, GenQAS increases success probability from

12\%

21\%

. In a noisy 6-qubit BeH

_2

transfer experiment, generative replay reduces the steps to chemical accuracy by

92.7\%

. These results show that generative replay can mitigate sample starvation in quantum architecture search and support more resource efficient circuit discovery.

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