Reinforcement Learning

Also known as RL

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144 papers in the last four weeks, up 243% on the four weeks before. 1.4% of all new papers.

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Latest papers 1,138

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ReSkill: Reconciling Skill Creation with Policy Optimization in Agentic RL
Jun 1, 2026Zelin He, Haotian Lin, Boran Han +6Reinforcement Learning LLM Agent Skill Learning
The Pennsylvania State University · † Work done during an internship at Amazon · Amazon IntelliHub +1
TRON: Targeted Rule-Verifiable Online Environments for Visual Reasoning RL
Jun 1, 2026Tianze Yang, Yucheng Shi, Ruitong Sun +3Reinforcement Learning VLM Reasoning
University of Georgia
RLVR without Ineffective Samples: Group Prioritized Off-Policy Optimization for LLM Reasoning
May 31, 2026Yixiu Mao, Yun Qu, Qi Wang +2Reinforcement Learning RL for Language Model Reasoning
Department of Automation, Tsinghua University
Lagrangian Perturbation Diffusion Steering: Latent Reinforcement Learning for Generative Policies
May 31, 2026Hikmet Simsir, Ozgur S. OguzRobot Policy Adaptation Reinforcement Learning
Department of Computer Engineering, Bilkent University, Ankara, Türkiye.
Deep Research as Rubric for Reinforcement Learning
May 31, 2026Wangyi Mei, Zhouhong Gu, Zhenhan Bai +9Open-Ended Generation Reinforcement Learning
Fudan University · Xiaohongshu Inc. · Beijing University of Posts and Telecommunications
Interaction-Limited Safe Continuous-Time RL for Dynamical Medical Treatment
May 31, 2026Xun Shen, Yuepeng Wang, Akifumi Wachi +13Reinforcement Learning Sequential Decision Making
Tokyo University of Agriculture and Technology · LY Corporation · National University of Singapore +6
A Unified Benchmark for Dynamic Medical Treatment Reinforcement Learning
May 31, 2026Yuepeng Wang, Ken Kawano, Yoshihiko Fujisawa +12RL Benchmarks Reinforcement Learning
Tokyo University of Agriculture and Technology · Institute of Science Tokyo · National University of Singapore +5
Explainable deep reinforcement learning reveals energy-efficient control strategies for turbulent drag reduction
May 31, 2026Federica Tonti, Ricardo VinuesaRL Control Reinforcement Learning
Department of Aerospace Engineering University of Michigan Ann Arbor, MI 48109, United States
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
Certificate-Guided Evaluation of Reinforcement Learning Generalization
May 30, 2026Vignesh Subramanian, Đorđe Žikelić, Suguman BansalRL Benchmarks Reinforcement Learning
School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA · School of Computing and Information Systems, Singapore Management University, Singapore
Decoupled Behavioral Cloning for Scalable Inductive Generalization in RL from Specifications
May 30, 2026Vignesh Subramanian, Subhajit Roy, Suguman BansalReinforcement Learning Meta-Reinforcement Learning
School of Computer Science, Georgia Institute of Technology, USA · Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, India
GIRL-DETR: Gradient-Isolated Reinforcement Learning for Video Moment Retrieval
May 30, 2026Shihang Zhang, Mingjin Kuai, Ye Wei +2Temporal Video Grounding Reinforcement Learning
Shihang Zhang and Ye Wei are with the College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China · Mingjin Kuai, Zhen Zhang, and Wei Ji are with the School of Intelligence Science and Technology, Nanjing University, Suzhou 215163, China
Regularized Offline Policy Optimization with Posterior Hybrid Bayesian Belief
May 30, 2026Hongqiang Lin, Pengfei Wang, Nenggan ZhengReinforcement Learning Bayesian RL
Zhejiang University, Hangzhou, China.
Cortex and subcortex play distinct roles over learning when cortical memory is limited
May 30, 2026Matthew Farrell, Taro ToyoizumiComputational Cognitive Modeling Reinforcement Learning
Laboratory for Neural Computation and Adaptation RIKEN Center for Brain Science, Wako, Japan · Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan.
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
Interpretable Policy Distillation for Power Grid Topology Control
May 30, 2026Aleksandra Dmitruka, Karlis FreivaldsRL Control Reinforcement Learning
University of Latvia, Faculty of Exact Sciences and Technology, Riga, Latvia
Reinforcement Learning with Pairwise Preferences in Long-Term Decision Problems
May 29, 2026Jonathan Colaço Carr, Prakash Panangaden, Doina Precup +1Pairwise Preference Learning Markov Decision Processes
Stanford University · School of Computer Science, McGill University, Montreal, Quebec, Canada · Mila - Quebec AI Institute, Montreal, Quebec, Canada +1
Drift Q-Learning
May 29, 2026Anas Houssaini, Mohamad H. Danesh, Amin Abyaneh +3Reinforcement Learning Q-Learning
McGill University · Mila - Quebec AI Institute
Physical Atari: A Robust and Accessible Platform for Real-time Reinforcement Learning on Robots
May 29, 2026Khurram Javed, Joseph Modayil, Gloria Kennickell +2RL Benchmarks Reinforcement Learning
Keen Technologies · Openmind Research Institute · University of Alberta, Canada
The Representation-Rationalizability Tradeoff in Reward Learning
May 29, 2026Jing Dong, Yaoliang Yu, Pascal PourpartPairwise Preference Learning Reward Modeling
Vector Institute · University of Waterloo & Vector Institute
Value Functions as Supermartingale Certificates
May 29, 2026Alessandro Abate, Daniel Contro, Mirco Giacobbe +2Reinforcement Learning Formal Verification
University of Oxford, UK · University of Birmingham, UK
DRIFT: Decoupled Rollouts and Importance-Weighted Fine-Tuning for Efficient Multi-Turn Optimization
May 29, 2026Jian Mu, Tianyi Lin, Chengwei Qin +2Supervised Fine-Tuning RL for Language Models
The Hong Kong University of Science and Technology (Guangzhou) · The Chinese University of Hong Kong, Shenzhen.
Answer-Set-Programming-based Abstractions for Reinforcement Learning
May 29, 2026Rafael Bankosegger, Thomas Eiter, Johannes OetschReinforcement Learning State Abstraction
Siemens AG Österreich, Vienna, Austria · TU Wien (Vienna University of Technology), Austria · Jönköping University, Sweden
Constrained Multi-Objective Reinforcement Learning with Max-Min Criterion
May 29, 2026Giseung Park, Hyunyoung Nam, Woohyeon Byeon +2Multi-Objective Reinforcement Learning Reinforcement Learning
Robotics Institute, University of Toronto, 55 St George St, Toronto, Canada. · School of Electical Engineering, KAIST, Daejeon 34141, Republic of Korea. · Faculty of Engineering, Bar-Ilan University, Ramat Gan 52900, Israel.
Survival Reinforcement Learning: Toward Scalable Self-Supervised RL
May 29, 2026Franki Nguimatsia-Tiofack, Fabian Schramm, Théotime Le Hellard +1Reinforcement Learning Goal-Conditioned RL
Inria and École Normale Supérieure PSL Research University Paris, France
EchoRL: Reinforcement Learning via Rollout Echoing
May 29, 2026Jinhe Bi, Aniri, Minglai Yang +9Reinforcement Learning RL for Language Model Reasoning
Huawei Heisenberg Research Center · LMU Munich · Munich Center for Machine Learning +5
Enhancing Human-Likeness in Reinforcement Learning Agents via Hierarchical Macro Action Quantization
May 29, 2026M. Shaheer Luqman, Usman Nizamani, Fawad Javed Fateh +4Latent Action Learning Reinforcement Learning
Retrocausal, Inc., Redmond, WA 98052, USA
Inverse Reinforcement Learning without an Optimal Demonstrator: A Feasible Reward Set Approach
May 29, 2026Kihyun Kim, Shripad Deshmukh, Nikos Vlassis +1Reinforcement Learning Learning from Demonstration
MIT LIDS · University of Massachusetts, Amherst · Adobe Research +1
Zero Collapse: A Failure Mode of Policy Gradient Methods in Discontinuous Reward Environments
May 29, 2026Nishant Kumar, Enrique Areyan Viqueira, Amy GreenwaldReinforcement Learning Policy Gradient Methods
DARTS: Distribution-Aware Active Rollout Trajectory Shaping for Accelerating LLM Reinforcement Learning
May 29, 2026Yujie Wang, Siwei Chen, Longzan Luo +4RL for Language Models Reinforcement Learning
School of Computer Science & Beijing Key Laboratory of Software and Hardware Cooperative Artificial Intelligence Systems, Peking University, Beijing, China · School of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai, China · Institute of Computational Social Science, Peking University (Qingdao), Qingdao, China

Jun 1, 2026, cs.AIJ/K move · Enter open · S save

ReSkill: Reconciling Skill Creation with Policy Optimization in Agentic RL

Zelin He, Haotian Lin, Boran Han, Wei Zhu+5

The Pennsylvania State University · † Work done during an internship at Amazon · Amazon IntelliHub+1

Reinforcement Learning LLM Agent Skill Learning RL for LLM Agents

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Agentic reinforcement learning (RL) enables LLM agents to improve continuously from environment rewards, yet the resulting policies do not systematically accumulate reusable strategies that generalize across tasks. Modular skills can provide such reusable strategies, yet existing skill-augmented RL methods decouple skill creation from policy optimization, risking adopting skills that conflict with the evolving policy. Inspired by Anthropic's Skill Creator, we introduce ReSkill, an RL-in-the-loop skill creation framework that reconciles skill evolution with policy learning. ReSkill exploits the group-wise structure of GRPO to naturally embed three mechanisms with only marginal additional overhead: (1) an assertion-driven skill creator that diagnoses failures from past experience and proposes conditional, trigger-based skill revisions; (2) within-group rollout sampling that enables controlled comparison of skill versions, capturing which version best supports the policy's ongoing learning; and (3) Thompson Sampling with adaptive discounting to balance exploration and exploitation in skill version selection as the policy evolves. Across several domains, ReSkill consistently outperforms existing memory and skill-based RL methods, with the largest gains on unseen tasks. Analysis of the skill lifecycle shows skills being automatically created, tested, refined, and pruned as the policy improves, demonstrating reconciled skill-policy co-evolution.

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Latest papers 1,138