Multi-Task RL

RL: Reinforcement Learning

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7 papers in the last four weeks, up 75% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 55

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Representation Learning Enables Scalable Multitask Deep Reinforcement Learning
Jun 4, 2026Johan Obando-Ceron, Lu Li, Scott Fujimoto +3Reinforcement Learning Representation Learning for RL
Mila – Québec AI Institute · Université de Montréal · McGill University +2
Generalizable Multi-Task Learning for Wireless Networks Using Prompt Decision Transformers
Jun 3, 2026Fatih Temiz, Shavbo Salehi, Melike Erol-KantarciReinforcement Learning Transformer
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada
A GPU-Parallel Framework for Heterogeneous Multi-Task Reinforcement Learning
Jun 2, 2026Rui Zhang, Qiwei Wu, Zhengyu Zhang +7Robotic RL Learning from Demonstration
NVIDIA · The Hong Kong University of Science and Technology (Guangzhou)
A Local Perturbation Theory for Cross-Domain Interference and Recovery in Multi-Domain RL
Jun 1, 2026Lei Yang, Siyu Ding, Deyi XiongRL for Language Models Reinforcement Learning
TJUNLP Lab, College of Intelligence and Computing, Tianjin University, Tianjin, China · Baidu Inc., Beijing, China
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
Reasoning Depth and Environment Complexity: A Controlled Study of RLVR Data Allocation across Logical Reasoning Tasks
May 26, 2026Yihua Zhu, Qianying Liu, Fei Cheng +4Logical Reasoning Abductive Reasoning
Kyoto University · NII LLMC · University of Tokyo +1
Adaptive Reinforcement Learning for Robust Open Quantum System Control: A Multi-Task Framework with Temporal Optimization
May 26, 2026Haftu W. Fentaw, Steve Campbell, Simon CatonRL Control Multi-Task RL
School of Computer Science, University College Dublin, Belfield, Dublin 4, Dublin, Ireland. · Centre for Quantum Engineering, Science, and Technology, University College Dublin, Belfield, Dublin 4, Dublin, Ireland. · School of Physics, University College Dublin, Belfield, Dublin 4, Dublin, Ireland.
Harmony in Diversity: Multi-domain Contrastive Policy Optimization for Large Reasoning Models
May 25, 2026Zongji Yu, Wenshui Luo, Yiliu Sun +5Reinforcement Learning RL for Language Model Reasoning
Shanghai Jiao Tong University · Shanghai AI Laboratory · Shandong University
GoLongRL: Capability-Oriented Long Context Reinforcement Learning with Multitask Alignment
May 19, 2026Minxuan Lv, Tiehua Mei, Tanlong Du +9Reinforcement Learning Long-Context Modeling
Kuaishou Technology · University of Chinese Academy of Sciences
DyGRO-VLA: Cross-Task Scaling of Vision-Language-Action Models via Dynamic Grouped Residual Optimization
May 17, 2026Sixu Lin, Yunpeng Qing, Litao Liu +4Vision-Language-Action Models Multi-Task RL
School of Data Science, The Chinese University of Hong Kong (Shenzhen) · Jiangxing Intelligence Technology Inc. · Zhejiang University +3
Distributionally Robust Multi-Task Reinforcement Learning via Adaptive Task Sampling
May 14, 2026Nicholas E. Corrado, Wenyuan Huang, Josiah P. HannaDistributionally Robust RL Multi-Task RL
Computer Sciences Department · University of Wisconsin – Madison
Multi-Objective and Mixed-Reward Reinforcement Learning via Reward-Decorrelated Policy Optimization
May 13, 2026Yang Bai, Kaiyuan Liu, Ziyuan Zhuang +5Multi-Objective Reinforcement Learning RL for Language Models
Meituan, China
TOPPO: Rethinking PPO for Multi-Task Reinforcement Learning with Critic Balancing
May 12, 2026Yuanpeng Li, Gefei Lin, Annie Qu +1Multi-Task RL Proximal Policy Optimization
UC Irvine · George Washington University · UC Santa Barbara +1
MARLaaS: Multi-Tenant Asynchronous Reinforcement Learning as a Service
May 8, 2026Timothy Tin Long Yu, Gursimran Singh, Ge Shi +3Asynchronous RL Reinforcement Learning
Huawei Technologies Canada
Schedule-and-Calibrate: Utility-Guided Multi-Task Reinforcement Learning for Code LLMs
May 7, 2026Yujia Chen, Yang Ye, Xiao Chu +2RL for Code Generation Reinforcement Learning
Harbin Institute of Technology, Shenzhen · Huawei Cloud Computing Technologies Co., Ltd.
Improving Zero-Shot Offline RL via Behavioral Task Sampling
Apr 28, 2026Nazim Bendib, Nicolas Perrin-Gilbert, Olivier SigaudReinforcement Learning Offline RL
Institut des Systèmes Intelligents et de Robotique, ISIR, Sorbonne Université, Paris, France.
Multi-action Tangled Program Graphs for Multi-task Reinforcement Learning with Continuous Control
Apr 28, 2026Quentin Vacher, Nicolas Beuve, Mickaël Dardaillon +1RL Benchmarks Genetic Programming
IETR · Univ Rennes, INSA Rennes, CNRS, IETR – UMR 6164, F-35000 Rennes, France
Task-specific Subnetwork Discovery in Reinforcement Learning for Autonomous Underwater Navigation
Apr 23, 2026Yi-Ling Liu, Melvin Laux, Mariela De Lucas Alvarez +2Reinforcement Learning Underwater Robotics
Robotics Innovation Center, German Research Center for Artificial Intelligence, Bremen, Germany · Robotics Research Group, University of Bremen, Bremen, Germany
RPG: Robust Policy Gating for Smooth Multi-Skill Transitions in Humanoid Fighting
Apr 23, 2026Yucheng Xin, Jiacheng Bao, Yubo Dong +5Robot Policy Learning Humanoid Robot Control
Center for Artificial Intelligence and Robotics, Shenzhen International Graduate School, Tsinghua University, China · Shanghai AI Laboratory, Shanghai Jiao Tong University
Reinforcement Learning Enabled Adaptive Multi-Task Control for Bipedal Soccer Robots
Apr 21, 2026Yulai Zhang, Yinrong Zhang, Ting Wu +1Legged Robotics Humanoid Robot Control
School of Future Technology, Shanghai University, Shanghai, China
To Mix or To Merge: Toward Multi-Domain Reinforcement Learning for Large Language Models
Feb 13, 2026Haoqing Wang, Xiang Long, Ziheng Li +3RL for Language Models Gradient Interference
Samsung Research, Beijing, China · Peking University
PlatoLTL: Scaling LTL-Guided Multi-Task RL
Jan 30, 2026Jacques Cloete, Mathias Jackermeier, Ioannis Havoutis +1RL for Robotics Constrained RL
Oxford Robotics Institute University of Oxford Oxford, United Kingdom · Department of Computer Science University of Oxford Oxford, United Kingdom
Enabling Energy-Efficient Simultaneous Multi-Task Reinforcement Learning through Spiking Neural Networks with Active Dendrites for Bio-inspired Generalist Agents
Dec 6, 2024Rachmad Vidya Wicaksana Putra, Avaneesh Devkota, Muhammad ShafiqueReinforcement Learning Deep Q-Learning
eBRAIN Lab · New York University (NYU) Abu Dhabi
DiffusionOPD: A Unified Perspective of On-Policy Distillation in Diffusion Models
Date pendingQuanhao Li, Junqiu Yu, Kaixun Jiang +7Diffusion Model Distillation Multi-Task RL
Fudan University, China · Alibaba Group, China
SkillX: Unified Multi-Skill Policy Learning for Humanoid Soccer
Date pendingZhangchen Ye, Enxuan Ruan, Yifei Bao +10Robot Skill Learning Humanoid Robot Control
Noetix Robotics · Tsinghua University

Jun 4, 2026, cs.LGJ/K move · Enter open · S save

Representation Learning Enables Scalable Multitask Deep Reinforcement Learning

Johan Obando-Ceron, Lu Li, Scott Fujimoto, Pierre-Luc Bacon+2

Mila – Québec AI Institute · Université de Montréal · McGill University+2

Reinforcement Learning Representation Learning for RL Continuous Control Multi-Task RL Model-Free RL

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Scaling reinforcement learning (RL) to diverse multitask settings remains a central challenge. While recent advances in model-based RL achieve strong performance, they rely on planning and complex training pipelines, making it unclear which components are essential for scalability. We revisit this question and argue that the primary driver of scalable multitask RL is not model-based control, but \emph{representation learning}. In particular, we show that combining predictive, model-based representations with high-capacity value function approximation is sufficient to achieve strong performance, even without planning. We evaluate a simple model-free algorithm, MR.Q, coupled with auxiliary predictive objectives into a scalable actor-critic architecture. This approach outperforms a recent world-model-based method and a range of deep RL baselines across a diverse suite of multitask continuous control tasks, while significantly reducing computational overhead and improving wall-clock efficiency. We observe consistent improvements with increased model capacity and show through ablations that predictive representation learning is critical for performance.

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