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

1,750 papers237 papers in the last four weeks, up 46% on the four weeks before. 1.4% of all new papers.

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
Reinforcement Learning via Value Gradient Flow
Apr 15, 2026Haoran Xu, Kaiwen Hu, Somayeh Sojoudi +1Regularized RL Reinforcement Learning
University of Texas at Austin · University of California, Berkeley
A Comparative Study of Dynamic Programming and Reinforcement Learning in Finite Horizon Dynamic Pricing
Apr 15, 2026Lev Razumovskiy, Nikolay KareninDynamic Pricing Reinforcement Learning
DiPO: Disentangled Perplexity Policy Optimization for Fine-grained Exploration-Exploitation Trade-Off
Apr 15, 2026Xiaofan Li, Ming Yang, Zhiyuan Ma +9Reinforcement Learning RL for Language Model Reasoning
East China Normal University · Ant Group · Xiamen University +1
RPS: Information Elicitation with Reinforcement Prompt Selection
Apr 15, 2026Tao Wang, Jingyao Lu, Xibo Wang +5LLM Prompting Reinforcement Learning
Department of Computer Science, Southwestern University of Finance and Economics, China · Eindhoven University of Technology, Netherlands · DreamSoul, China +4
Soft $Q(λ)$ : A multi-step off-policy method for entropy regularised reinforcement learning using eligibility traces
Apr 15, 2026Pranav Mahajan, Ben SeymourReinforcement Learning Q-Learning
University of Oxford
MM-Doc-R1: Training Agents for Long Document Visual Question Answering through Multi-turn Reinforcement Learning
Apr 15, 2026Jiahang Lin, Kai Hu, Binghai Wang +12Long-Document Visual Question Answering Multi-Turn RL
Fudan University · Shanghai Qiji Zhifeng Co., Ltd
Representation over Routing: Diagnosing Temporal Routing Pathologies in Multi-Timescale PPO
Apr 15, 2026Jing SunReinforcement Learning Credit Assignment in RL
Information Engineering School, Chengyi College, Jimei University, Xiamen 361000
Optimistic Policy Learning under Pessimistic Adversaries with Regret and Violation Guarantees
Apr 15, 2026Sourav Ganguly, Kartik Pandit, Arnob GhoshAdversarial MARL Reinforcement Learning
Dept. of Electrical and Computer Engineering, NJIT, Newark, NJ, USA
Cycle-Consistent Search: Question Reconstructability as a Proxy Reward for Search Agent Training
Apr 14, 2026Sohyun An, Shuibenyang Yuan, Hayeon Lee +2Unsupervised RL Cycle-Consistent Learning
Meta Superintelligence Labs · UCLA
Balanced Aggregation: Understanding and Fixing Aggregation Bias in GRPO
Apr 14, 2026Zhiyuan Zeng, Jiameng Huang, Zhangyue Yin +8Reinforcement Learning RL for Language Model Reasoning
Fudan University · M-A-P · Peking University +3
Automated co-design of high-performance thermodynamic cycles via graph-based hierarchical reinforcement learning
Apr 14, 2026Wenqing Li, Xu Feng, Peixue Jiang +1Reinforcement Learning RL for Combinatorial Optimization
Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Beijing Key Laboratory of CO2 Utilization and Reduction Technology, Department of Energy and Power Engineering, Tsinghua University, Beijing, 100084, China · Shanxi Research Institute for Clear Energy Tsinghua University, Taiyuan, 030032, China
Self-Monitoring Benefits from Structural Integration: Lessons from Metacognition in Continuous-Time Multi-Timescale Agents
Apr 13, 2026Ying XieReinforcement Learning
Kennesaw State University
Utilizing and Calibrating Hindsight Process Rewards via Reinforcement with Mutual Information Self-Evaluation
Apr 13, 2026Jiashu Yao, Heyan Huang, Zeming Liu +1Reinforcement Learning RL for LLM Agents
Beijing Institute of Technology · Beihang University
Relax: An Asynchronous Reinforcement Learning Engine for Omni-Modal Post-Training at Scale
Apr 13, 2026Liujie Zhang, Benzhe Ning, Rui Yang +8Reinforcement Learning RL Post-Training
AI Platform, Xiaohongshu Inc · The University of Hong Kong · University of Science and Technology of China
Eliciting Medical Reasoning with Knowledge-enhanced Data Synthesis: A Semi-Supervised Reinforcement Learning Approach
Apr 13, 2026Haolin Li, Shuyang Jiang, Ruipeng Zhang +3Medical LLMs RL for Language Model Reasoning
College of Computer Science and Artificial Intelligence, Fudan University · Shanghai AI Laboratory · Department of Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine +3
Policy Split: Incentivizing Dual-Mode Exploration in LLM Reinforcement with Dual-Mode Entropy Regularization
Apr 13, 2026Jiashu Yao, Heyan Huang, Daiqing Wu +2RL for Language Models Reinforcement Learning
Beijing Institute of Technology · Tsinghua University · Beihang University
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

Apr 18, 2026J/K move · Enter open · S save

EasyVideoR1: Easier RL for Video Understanding

Chuanyu Qin, Chenxu Yang, Qingyi Si, Naibin Gu+5

Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China · School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

Reinforcement Learning Video-Language Models Reinforcement Learning with Verifiable Rewards RL for VLMs RL for Video Reasoning

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Reinforcement learning from verifiable rewards (RLVR) has demonstrated remarkable effectiveness in improving the reasoning capabilities of large language models. As models evolve into natively multimodal architectures, extending RLVR to video understanding becomes increasingly important yet remains largely unexplored, due to the diversity of video task types, the computational overhead of repeatedly decoding and preprocessing high-dimensional visual inputs, and the difficulty of reproducible evaluation across numerous sensitive hyperparameters. Existing open-source RL training frameworks provide solid infrastructure for text and image scenarios but lack systematic optimizations tailored for video modality. In this work, we present EasyVideoR1, a complete and efficient reinforcement learning framework specifically designed for training large vision-language models on video understanding tasks. EasyVideoR1 makes the following contributions: (1) a full video RL training pipeline with offline preprocessing and tensor caching that eliminates redundant video decoding and yields a 1.47

\times

throughput improvement; (2) a comprehensive, task-aware reward system covering 11 distinct video and image problem types with unified routing and modular extension; (3) a mixed offline-online data training paradigm that combines curated high-quality trajectories with on-policy exploration, benefiting the learning of more challenging tasks; (4) joint image-video training with independently configurable pixel budgets, allowing the two modalities to mutually reinforce each other; and (5) an asynchronous multi-benchmark evaluation framework covering 22 mainstream video understanding benchmarks, with reproduced accuracy closely aligned with officially reported scores.