Continual RL

RL: Reinforcement Learning

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4 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Continual Graph Multi-Agent Reinforcement Learning
Oct 7, 2026Tommaso Marzi, Ahmed Hendawy, Jan Peters +3Multi-Agent Reinforcement Learning Continual RL
IDSIA USI-SUPSI, Università della Svizzera italiana, Lugano, Switzerland · Technical University of Darmstadt, Darmstadt, Germany · Robotics Institute Germany (RIG) +4
Off-Policy Merging Beats On-Policy Self-Distillation for Continual Learning
Oct 5, 2026Chen Henry Wu, Thomas Zhang, Aditi RaghunathanContinual Learning for LLMs On-Policy Distillation
Carnegie Mellon University
Continual Reinforcement Learning with Neuroevolution
Oct 1, 2026Eleni Nisioti, Andrea Cossu, Kathrin Korte +1Stability-Plasticity Dilemma Loss of Plasticity
IT University of Copenhagen · University of Pisa · Sakana AI
When Validation Stops Learning: Auditing Update Admission for Continual Embodied Agents
Sep 12, 2026Qinzhen Ma, Ruihai WuContinual Robot Learning Safe RL
Rice University · University of California, Berkeley
A Bellman Optimality Equation for Plasticity
Sep 11, 2026Jeremy Lucas, Doina PrecupStability-Plasticity Dilemma Reinforcement Learning
Mila – Quebec Artificial Intelligence Institute Montreal, Quebec, Canada
Measuring the Value of World-Model Updates: A Counterfactual Utility Protocol for Continual Adaptation
Sep 11, 2026Anqi Peter Li, Kaden KimCounterfactual Evaluation World Model Learning
Substrate Labs · UC Berkeley
Beyond Forgetting: Diagnosing and Harnessing Shared Reasoning in Continual RLVR
Aug 19, 2026Lirui Luo, Guoxi Zhang, Hongming Xu +3RL for Language Model Reasoning Replay-Based Continual Learning
State Key Lab of General AI, School of Intelligence Science and Technology, Peking University · State Key Laboratory of General Artificial Intelligence, BIGAI · Beijing Institute of Technology +1
Catastrophic Forgetting in Continual Reinforcement Learning
Aug 9, 2026Emma GrahamContinual RL Catastrophic Forgetting
UCL · Machine Learning MSc · [cs.LG]
ATLAS: Adaptive Topological Learning with Abstract Successors for Continual Learning
Aug 5, 2026R. Blake Lawlor, Daniel S. BrownNon-Stationary RL Reinforcement Learning
1Kahlert School of Computing, University of Utah
Verifier-Induced Support Reshaping in On-Policy Optimization
Jul 31, 2026Shaohang Wei, Zikun Su, Feifan Song +4RL for Language Model Reasoning Reinforcement Learning with Verifiable Rewards
Peking University · BUPT
Calibrated Partial Resets: Preventing Policy Collapse in Continual Reinforcement Learning
Jul 27, 2026Luc McCutcheon, Evangelos Chatzaroulas, Saber FallahNon-Stationary RL Neural Network Initialization
Department of Mechanical Engineering, University of Surrey, United Kingdom · Department of Computing Science, University of Surrey, United Kingdom
Continual-RL for Generalization in Autonomous Racing on the RoboRacer Platform
Jul 27, 2026Joel Siegert, Edoardo Ghignone, Michele MagnoBackpropagation Continual Robot Learning
Center for Project-Based Learning, D-ITET, ETH Zurich
NeuroSynth: A Biologically Inspired Continual Reinforcement Learning Architecture for Mitigating Catastrophic Forgetting
Jul 24, 2026Yash KiniContinual Learning Continual RL
James Madison High School Vienna, Virginia, USA
Adaptive Multi-Horizon Reinforcement Learning
Jul 22, 2026Manoosh Samiei, Doina Precup, Paul MassetContinual RL
Department of Computer Science, McGill University, Canada · 2Mila - Quebec Artificial Intelligence Institute · Department of Psychology, McGill University, Canada
Extreme-RGMT: Continual Learning of Highly Dynamic Skills for Robust Generalist Humanoid Control
Jul 22, 2026Yubiao Ma, Han Yu, Kai Guo +5Continual Robot Learning Robot Skill Learning
School of Automation, Beijing Institute of Technology, Beijing 100081, China. · Humanoid Robotics (Shanghai) Co., Ltd., Shanghai 201203, China. · School of Mechanical Engineering, Shandong University, Jinan 250061, China.
The World Model Remembers, the Actor Forgets: Dream Rehearsal for Continual Model-Based RL
Jul 22, 2026Gurp NijjerGenerative Replay Reinforcement Learning
Quantegra Research, Canada
RL Forgets! Towards Continual Policy Optimization
Jul 5, 2026Mao-Lin Luo, Zhe-Xu Wang, Zi-Hao Zhou +4RL Post-Training Continual Learning for VLMs
School of Computer Science and Engineering, Southeast University, Nanjing 210096, China · Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China · Zhongguancun Academy +1
FAR: Failure-Aware Retry for Test-Time Recovery and Continual Policy Improvement
Jul 1, 2026Haoran Hao, Shahram Najam Syed, Jeffrey Ichnowski +1Robot Policy Learning Robot Policy Adaptation
Carnegie Mellon University
Learning from Mistakes: Rollout-Retrieval Lifelong Policy Learning for Autonomous Driving
Jun 29, 2026Cheng Gong, Haoyang Wang, Chao Lu +2Autonomous Driving Planning Continual RL
School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China · School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798
Continual Robot Policy Learning via Variational Neural Dynamics
Jun 25, 2026Jiaxu Xing, Zhiyuan Zhu, Yunfan Ren +4Robot Policy Learning Robot Policy Adaptation
Robotics and Perception Group, University of Zurich
Stagnant Neuron: Towards Understanding the Plasticity Loss in Multi-Agent Reinforcement Learning Value Factorization Methods
Jun 24, 2026Zhengzhu Liu, Zeming Gao, Haoyuan Qin +7Loss of Plasticity Multi-Agent Reinforcement Learning
Fujian Key Laboratory of Urban Intelligent Sensing and Computing, School of Informatics, Xiamen University (XMU), China · Key Laboratory of Multimedia Trusted Perception and Efficient Computing, XMU, China
Self-Improvement Can Self-Regress: The Rise-and-Collapse Failure Mode of LLM Self-Training
Jun 17, 2026Jianzhe LinRL for Code Generation Reinforcement Learning
MetaAI · *Work done at Meta
When Robots Sleep: Offline Skill Consolidation for Shared-Policy Robot Learning
Jun 16, 2026Nethmi Jayasinghe, Diana Gontero, Amit Ranjan TrivediRobot Policy Learning Continual Robot Learning
Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL, USA.
Position: Deployed Reinforcement Learning should be Continual
Jun 1, 2026Parnian Behdin, Kevin Roice, Golnaz MesbahiNon-Stationary RL Reinforcement Learning
Alberta Machine Intelligence Institute (Amii), Edmonton, Canada.
Task diversity produces systematic transfer but inhibits continual reinforcement learning
May 30, 2026Purab Seth, Neil Shah, Ishaan Sinha +4RL Benchmarks Reinforcement Learning
Harvard College · University of Washington · Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University +1
CARE-RL: Capability-Aware Reinforcement Learning for Mitigating Cross-Domain Conflicts
May 30, 2026Rui Zhang, Xinle Wu, Yao LuReward Modeling Reinforcement Learning
National University of Singapore Singapore
Balancing Plasticity and Stability with Fast and Slow Successor Features
May 25, 2026Raymond Chua, Doina Precup, Blake RichardsStability-Plasticity Dilemma Non-Stationary RL
School of Computer Science, McGill University, Montr´eal, Canada · Mila - Quebec Artificial Intelligence Institute, Montr´eal, QC, Canada · CIFAR Learning in Machines and Brains +3
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
Safe Continual Reinforcement Learning under Nonstationarity via Adaptive Safety Constraints
May 13, 2026Timofey TomashevskiyNon-Stationary RL Reinforcement Learning
McMaster Centre for Software Certification Department of Computing and Software McMaster University, Canada
SPHERE: Mitigating the Loss of Spectral Plasticity in Mixture-of-Experts for Deep Reinforcement Learning
May 6, 2026Lirui Luo, Guoxi Zhang, Hongming Xu +2Loss of Plasticity Reinforcement Learning
State Key Lab of General AI, School of Intelligence Science and Technology, Peking University · State Key Laboratory of General Artificial Intelligence, BIGAI · Institute for Artificial Intelligence, Peking University

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Continual Graph Multi-Agent Reinforcement Learning

Tommaso Marzi, Ahmed Hendawy, Jan Peters, Carlo D'Eramo+2

IDSIA USI-SUPSI, Università della Svizzera italiana, Lugano, Switzerland · Technical University of Darmstadt, Darmstadt, Germany · Robotics Institute Germany (RIG)+4

Multi-Agent Reinforcement Learning Continual RL Cooperative MARL

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In Continual Multi-Agent Reinforcement Learning (CMARL), agents learn cooperative policies across sequences of tasks, aiming to adapt effectively to new tasks while preserving the ability to solve previously encountered ones. In many applications, tasks differ in their underlying structure, which can represent, for example, distinct operational conditions or target configurations (e.g., different network topologies in power grids or arrangements in formation control). Existing CMARL methods lack dedicated mechanisms to leverage this structural information when learning new tasks, failing to promote transfer and mitigate forgetting. To fill this gap, we propose Continual Graph Multi-Agent Reinforcement Learning (CGMARL), a novel framework for CMARL problems in which task sequences are mapped into a series of attributed graphs, each modeling a task-specific structure. In CGMARL, each graph determines the environment dynamics (next states and/or rewards) and the number of agents for the corresponding task. Then, we present Graph-based Formation (GRAFO), the first CGMARL benchmark, and show how forgetting arises in this setting. Finally, to address this limitation, we propose Frozen Graph Encoder (FROG), a method that relies on a frozen graph backbone to preserve past structural information in graph-based CMARL policies. Experiments on GRAFO show that pairing FROG with existing CL methods substantially improves performance on multiple CGMARL scenarios.

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