Reinforcement Learning

Also known as RL

Reinforcement learning studies how an agent can choose actions over time to earn rewards, using experience to improve its decisions. Common approaches include value-based methods such as Q-learning, policy-gradient methods such as PPO, and model-based methods that learn or use a model of the environment; offline and hierarchical methods address learning from fixed data and long tasks. Applications range from Atari and MuJoCo benchmarks to robot control, traffic, games, and language-model agents that use tools or solve multi-step problems. A central challenge is making learning efficient and reliable when rewards are sparse or imperfect, environments change, and success in simulation or benchmarks may not transfer safely to deployment.

Latest papers

1,767 papers195 papers in the last four weeks, down 8% on the four weeks before. 1.3% of all new papers.

Beyond Bellman: High-Order Generator Regression for Continuous-Time Policy Evaluation
Apr 21, 2026Yaowei Zheng, Richong Zhang, Shenxi Wu +5Reinforcement Learning Value Function Estimation
School of Computer Science and Engineering, Beihang University, China · Fudan University, China · Faculty of Humanities and Social Sciences, University of Nottingham, China +1
Curiosity-Critic: Cumulative Prediction Error Improvement as a Tractable Intrinsic Reward for World Model Training
Apr 20, 2026Vin Bhaskara, Haicheng WangIntrinsic Reward Methods Reinforcement Learning
Department of Computer Science, University of Toronto, Toronto, Canada.
Bounded Ratio Reinforcement Learning
Apr 20, 2026Yunke Ao, Le Chen, Bruce D. Lee +5Reinforcement Learning Group Relative Policy Optimization
ETH Zurich · MPI for Intelligent Systems · University of Alberta +2
Too Correct to Learn: Reinforcement Learning on Saturated Reasoning Data
Apr 20, 2026Zhenwen Liang, Yujun Zhou, Sidi Lu +3Reinforcement Learning RL for Language Model Reasoning
Tencent AI Lab · University of Notre Dame
Negative Advantages Is a Double-Edged Sword: Calibrating advantages in GRPO for Search Agents
Apr 20, 2026Jiayi Wu, Ruobing Xie, Zeqian Huang +6Reinforcement Learning RL for Language Model Reasoning
School of Data Science and Engineering, East China Normal University · Tencent · Tsinghua University
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 Self-Supervised RL
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 +5Entropy Collapse in RL Reinforcement Learning
College of Software, Nankai University · Zhongguancun Academy · Shanghai Jiao Tong University +3
Scouting By Reward: VLM-TO-IRL-Driven Player Selection For Esports
Apr 15, 2026Qing Yan, Wenyu Yang, Yufei Wang +4Reinforcement Learning
Johns Hopkins University · University of Pennsylvania · Cornell University
Aerial Multi-Functional RIS in Fluid Antennas-Aided Full-Duplex Networks: A Self-Optimized Hybrid Deep Reinforcement Learning Approach
Apr 15, 2026Li-Hsiang Shen, Yu-Quan ZhengHybrid Action Space RL Wireless Communications
Department of Communication Engineering, National Central University, Taoyuan 320317, Taiwan
From $P(y|x)$ to $P(y)$ : Investigating Reinforcement Learning in Pre-train Space
Apr 15, 2026Yuqiao Tan, Minzheng Wang, Bo Liu +5Reinforcement Learning RL for Language Model Reasoning
Institute of Automation, Chinese Academy of Sciences · University of Chinese Academy of Sciences · National University of Singapore +1

Apr 21, 2026J/K move · Enter open · S save

Beyond Bellman: High-Order Generator Regression for Continuous-Time Policy Evaluation

Yaowei Zheng, Richong Zhang, Shenxi Wu, Shirui Bian+4

School of Computer Science and Engineering, Beihang University, China · Fudan University, China · Faculty of Humanities and Social Sciences, University of Nottingham, China+1

Reinforcement Learning Value Function Estimation

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We study finite-horizon continuous-time policy evaluation from discrete closed-loop trajectories under time-inhomogeneous dynamics. The target value surface solves a backward parabolic equation, but the Bellman baseline obtained from one-step recursion is only first-order in the grid width. We estimate the time-dependent generator from multi-step transitions using moment-matching coefficients that cancel lower-order truncation terms, and combine the resulting surrogate with backward regression. The main theory gives an end-to-end decomposition into generator misspecification, projection error, pooling bias, finite-sample error, and start-up error, together with a decision-frequency regime map explaining when higher-order gains should be visible. Across calibration studies, four-scale benchmarks, feature and start-up ablations, and gain-mismatch stress tests, the second-order estimator consistently improves on the Bellman baseline and remains stable in the regime where the theory predicts visible gains. These results position high-order generator regression as an interpretable continuous-time policy-evaluation method with a clear operating region.