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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Physics-Informed Causal MDPs for Sequential Constraint Repair in Engineering Simulation Pipelines
Apr 20, 2026Chuhan QiaoMarkov Decision Processes Reinforcement Learning
Beijing JiaoTong University
LEPO: Latent Reasoning Policy Optimization for Large Language Models
Apr 20, 2026Yuyan Zhou, Jiarui Yu, Hande Dong +4Reinforcement Learning RL for Language Model Reasoning
Tencent · Zhejiang University · Shanghai Jiao Tong University
Poly-EPO: Training Exploratory Reasoning Models
Apr 19, 2026Ifdita Hasan Orney, Jubayer Ibn Hamid, Shreya S Ramanujam +5Reinforcement Learning RL for Language Model Reasoning
Stanford University
CoSearch: Joint Training of Reasoning and Document Ranking via Reinforcement Learning for Agentic Search
Apr 19, 2026Hansi Zeng, Liam Collins, Bhuvesh Kumar +2Reinforcement Learning Learning to Rank
Center for Intelligent Information Retrieval, University of Massachusetts Amherst · Snap Inc.
SVL: Goal-Conditioned Reinforcement Learning as Survival Learning
Apr 19, 2026Franki Nguimatsia Tiofack, Fabian Schramm, Théotime Le Hellard +1Reinforcement Learning Goal-Conditioned RL
Inria and École Normale Supérieure, PSL Research University, Paris, France.
Waking Up Blind: Cold-Start Optimization of Supervision-Free Agentic Trajectories for Grounded Visual Perception
Apr 19, 2026Ashutosh Bajpai, Tamal Majumder, Akshay Nambi +1Reinforcement Learning Tool-Using Vision-Language Agents
Indian Institute of Technology Delhi, India · Microsoft Research, India · Indian Institute of Technology Abu Dhabi, UAE
Beyond the Bellman Fixed Point: Geometry and Fast Policy Identification in Value Iteration
Apr 19, 2026Donghwan LeeMarkov Decision Processes Reinforcement Learning
Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, South Korea
Rethinking the Comparison Unit in Sequence-Level Reinforcement Learning: An Equal-Length Paired Training Framework from Loss Correction to Sample Construction
Apr 19, 2026Fei Ding, Yongkang Zhang, Runhao Liu +5RL for Language Models Reinforcement Learning
Alibaba Group · Tsinghua University
Live LTL Progress Tracking: Towards Task-Based Exploration
Apr 18, 2026Noel Brindise, Cedric Langbort, Melkior OrnikReinforcement Learning Linear Temporal Logic
Coordinated Science Laboratory, University of Illinois Urbana-Champaign, Urbana, USA
SPS: Steering Probability Squeezing for Better Exploration in Reinforcement Learning for Large Language Models
Apr 18, 2026Yifu Huo, Chenglong Wang, Ziming Zhu +9Reinforcement Learning RL for Language Model Reasoning
1Northeastern University, Shenyang, China · 2CAS Key Laboratory of Behavioral Science, Beijing, China · 3Independent Researcher, Beijing, China +1
NaviFormer: A Deep Reinforcement Learning Transformer-like Model to Holistically Solve the Navigation Problem
Apr 18, 2026Daniel Fuertes, Andrea Cavallaro, Carlos R. del-Blanco +2Reinforcement Learning Robot Navigation
Grupo de Tratamiento de Im´agenes, Information Processing and Telecommunications Center, ETSI Telecomunicaci´on, Universidad Polit´ecnica de Madrid, 28040 Madrid, Spain · Idiap Research Institute, Martigny, Switzerland
Freshness-Aware Prioritized Experience Replay for LLM/VLM Reinforcement Learning
Apr 18, 2026Weiyu Ma, Yongcheng Zeng, Yan Song +4Reinforcement Learning RL Post-Training
King Abdullah University of Science and Technology (KAUST) · Chinese Academy of Sciences, Institute of Automation (CASIA) · AI Centre, Department of Computer Science, University College London +1
EasyVideoR1: Easier RL for Video Understanding
Apr 18, 2026Chuanyu Qin, Chenxu Yang, Qingyi Si +6Reinforcement Learning Video-Language Models
Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China · School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China
GRAIL: Autonomous Concept Grounding for Neuro-Symbolic Reinforcement Learning
Apr 18, 2026Hikaru Shindo, Henri Rößler, Quentin Delfosse +1Reinforcement Learning LLM-Guided RL
Technical University of Darmstadt · Google Intrinsic · German Research Center for Artificial Intelligence (DFKI) +1
Beyond Distribution Sharpening: The Importance of Task Rewards
Apr 17, 2026Sarthak Mittal, Leo Gagnon, Guillaume LajoieReinforcement Learning RL for Language Model Reasoning
1Mila · 2Universit´e de Montr´eal
Multi-objective Reinforcement Learning With Augmented States Requires Rewards After Deployment
Apr 17, 2026Peter Vamplew, Cameron FoaleMulti-Objective Reinforcement Learning Reinforcement Learning
Institute of Innovation, Science and Sustainability, Federation University Australia, University Drive, Mt Helen, 3350, Victoria, Australia
Flexible Empowerment at Reasoning with Extended Best-of-N Sampling
Apr 17, 2026Taisuke KobayashiReinforcement Learning Best-of-N Sampling
National Institute of Informatics and The Graduate University for Advanced Studies, SOKENDAI Japan
CSLE: A Reinforcement Learning Platform for Autonomous Security Management
Apr 16, 2026Kim HammarReinforcement Learning Autonomous Cyber Defense
Division of Network and Systems Engineering, KTH Royal Institute of Technology, Sweden
Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning
Apr 16, 2026Lute Lillo, Nick CheneyNon-Stationary RL Reinforcement Learning
Department of Computer Science University of Vermont USA
LongAct: Harnessing Intrinsic Activation Patterns for Long-Context Reinforcement Learning
Apr 16, 2026Bowen Ping, Zijun Chen, Tingfeng Hui +4Reinforcement Learning RL for Language Model Reasoning
1Peking University · 2Shanghai Jiao Tong University · 3Beijing University of Posts and Telecommunications
Wasserstein Formulation of Reinforcement Learning. An Optimal Transport Perspective on Policy Optimization
Apr 16, 2026Mathias DusReinforcement Learning Wasserstein Gradient Flows
IRMA
Targeted Exploration via Unified Entropy Control for Reinforcement Learning
Apr 16, 2026Chen Wang, Lai Wei, Yanzhi Zhang +5Reinforcement Learning RL for Language Model Reasoning
College of Software, Nankai University · Zhongguancun Academy · Shanghai Jiao Tong University +3
Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers
Apr 13, 2026I. Esra BuyuktahtakinReinforcement Learning Predict-Then-Optimize
Grado Department of Industrial and Systems Engineering, Virginia Tech Blacksburg, VA 24061, USA
Less Data Approximates More: Earning Faithful Confidence in High-Stakes Domains
Apr 9, 2026Haokai Ma, Lee Yan Zhen, Gang Yang +4RL for Language Models Reinforcement Learning
National University of Singapore, Singapore · Singapore Management University, Singapore
Incremental Residual Reinforcement Learning Toward Real-World Learning for Social Navigation
Apr 9, 2026Haruto Nagahisa, Kohei Matsumoto, Yuki Tomita +2Reinforcement Learning Mobile Robotics
Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan · Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan
ReBound: Reset-Free Reinforcement Learning for Agile Driving via Reset-Aware Semi-Markov Bootstrapping
Apr 9, 2026Kohei Honda, Aiki Kada, Hirotaka HosogayaReinforcement Learning RL for Autonomous Driving
Department of Mechanical Systems Engineering, Nagoya University, Aichi, Japan · CyberAgent AI Lab, Tokyo, Japan
An Imperfect Verifier is Good Enough: Learning with Noisy Rewards
Apr 9, 2026Andreas Plesner, Francisco Guzmán, Anish AthalyeRL for Code Generation Reinforcement Learning
Handshake AI · ETH Zurich
Value Mirror Descent for Reinforcement Learning
Apr 7, 2026Zhichao Jia, Guanghui LanMarkov Decision Processes Reinforcement Learning
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332.
Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning
Apr 2, 2026Yuhang Wu, Xiangqing Shen, Fanfan Wang +4Retrieval-Augmented Generation Reinforcement Learning
School of Computer Science and Engineering, Nanjing University of Science and Technology, China · School of Intelligence Science and Technology, Nanjing University, China · School of Artificial Intelligence, Nanjing University, China
Planning for Change: Reinforcement Learning Combined with Bounded Extremum Seeking for Robotic Control under Distribution Shift
Apr 1, 2026Shaifalee Saxena, Rafael Fierro, Alexander ScheinkerRobotic Control Reinforcement Learning
Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87106, USA · Los Alamos National Lab, Los Alamos, NM 87547, USA

Apr 20, 2026, cs.AIJ/K move · Enter open · S save

Physics-Informed Causal MDPs for Sequential Constraint Repair in Engineering Simulation Pipelines

Chuhan Qiao

Beijing JiaoTong University

Markov Decision Processes Reinforcement Learning Constrained RL Causal Inference

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Off-policy learning in constrained MDPs with large binary state spaces faces a fundamental tension: causal identification of transition dynamics requires structural assumptions, while sample-efficient policy learning requires state-space compression. We introduce PI-CMDP, a framework for CMDPs whose constraint dependencies form a layered DAG under a Lifecycle Ordering Assumption (LOA). We propose an Identify-Compress-Estimate pipeline: (i) Identify: LOA enables backdoor identification of causal edge weights for cross-layer pairs, with formal partial-identification bounds when LOA is violated; (ii) Compress: a Markov abstraction compresses state cardinality from 2^(WL) to (W+1)^L under layer-priority regularity and exchangeability; and (iii) Estimate: a physics-guided doubly-robust estimator remains unbiased and reduces the variance constant when the physics prior outperforms a learned model. We instantiate PI-CMDP on constraint repair in engineering simulation pipelines. On the TPS benchmark (4,206 episodes), PI-CMDP achieves 76.2% repair success rate with only 300 training episodes versus 70.8% for the strongest baseline (+5.4 pp), narrowing to +2.8 pp (83.4% vs. 80.6%) in the full-data regime, while substantially reducing cascade failure rates. All improvements are consistent across 5 independent seeds (paired t-test p < 0.02).

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Latest papers 1,138