Key papers

• DAPO: An Open-Source LLM Reinforcement Learning System at Scale (2025)
  Qiying Yu et al.
  18.33
• We-Math 2.0: A Versatile MathBook System for Incentivizing Visual Mathematical Reasoning (2025)
  Runqi Qiao and Qiuna Tan and Peiqing Yang and Yanzi Wang and Xiaowan Wang and Enhui Wan and Sitong Zhou and Guanting Dong and Yuchen Zeng and Yida Xu and Jie Wang and Chong Sun and Chen Li and Honggang Zhang
  16.20
• Explainable Deep Reinforcement Learning: State Of The Art And Challenges (2023)
  George A. Vouros
  15.80
• SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild (2025)
  Weihao Zeng et al.
  15.29
• Control Synthesis from Linear Temporal Logic Specifications using Model-Free Reinforcement Learning (2019)
  Alper Kamil Bozkurt et al.
  14.93
• GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning (2025)
  GLM-V Team: Wenyi Hong et al.
  13.62
• R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning (2025)
  Huatong Song et al.
  13.61
• Bayesian policy gradient and actor-critic algorithms (2026)
  Mohammad Ghavamzadeh et al.
  13.41
• R1-Zero's "Aha Moment" in Visual Reasoning on a 2B Non-SFT Model (2025)
  Hengguang Zhou and Xirui Li and Ruochen Wang and Minhao Cheng and Tianyi Zhou and Cho-Jui Hsieh
  12.65
• CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning (2018)
  C\'edric Colas et al.
  12.25
• ArenaRL: Scaling RL for Open-Ended Agents via Tournament-based Relative Ranking (2026)
  Qiang Zhang et al.
  11.79
• R1-Omni: Explainable Omni-Multimodal Emotion Recognition with Reinforcement Learning (2025)
  Jiaxing Zhao et al.
  11.76
• Reasoning or Memorization? Unreliable Results of Reinforcement Learning Due to Data Contamination (2025)
  Mingqi Wu et al.
  11.09
• RLPR: Extrapolating RLVR to General Domains without Verifiers (2025)
  Tianyu Yu et al.
  11.06
• Deep Q-learning: Theoretical Insights From An Asymptotic Analysis (2020)
  Arunselvan Ramaswamy, Eyke Hüllermeier
  10.35
• Defeating the Training-Inference Mismatch via FP16 (2025)
  Penghui Qi et al.
  9.69
• BuildingGym: An open-source toolbox for AI-based building energy management using reinforcement learning (2025)
  Xilei Dai et al.
  9.35
• Scaling RL to Long Videos (2025)
  Yukang Chen et al.
  9.07
• NFT: Bridging Supervised Learning and Reinforcement Learning in Math Reasoning (2025)
  Huayu Chen et al.
  8.98
• Beyond Distillation: Pushing the Limits of Medical LLM Reasoning with Minimalist Rule-Based RL (2025)
  Che Liu et al.
  8.90
• QuestA: Expanding Reasoning Capacity in LLMs via Question Augmentation (2025)
  Jiazheng Li et al.
  8.84
• Stable Reinforcement Learning for Efficient Reasoning (2025)
  Muzhi Dai and Shixuan Liu and Qingyi Si
  8.73
• A reinforcement learning agent for maintenance of deteriorating systems with increasingly imperfect repairs (2025)
  Alberto Pliego Marug\'an et al.
  8.64
• Every Step Evolves: Scaling Reinforcement Learning for Trillion-Scale Thinking Model (2025)
  Ling Team et al.
  8.51
• Neglected Free Lunch from Post-training: Progress Advantage for LLM Agents (2026)
  Changdae Oh et al.
  8.17
• CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation (2026)
  Weinan Dai et al.
  8.12
• Research on Edge Computing and Cloud Collaborative Resource Scheduling Optimization Based on Deep Reinforcement Learning (2025)
  Yuqing Wang et al.
  7.96
• A Simulation Benchmark for Autonomous Racing with Large-Scale Human Data (2024)
  Adrian Remonda et al.
  7.50
• UI-S1: Advancing GUI Automation via Semi-online Reinforcement Learning (2025)
  Zhengxi Lu et al.
  7.35
• VCRL: Variance-based Curriculum Reinforcement Learning for Large Language Models (2025)
  Guochao Jiang et al.
  7.35
• Extensions of Robbins-Siegmund Theorem with Applications in Reinforcement Learning (2025)
  Xinyu Liu et al.
  7.30
• Adaptive 3D UI Placement in Mixed Reality Using Deep Reinforcement Learning (2025)
  Feiyu Lu et al.
  7.24
• CCLF: A Contrastive-curiosity-driven Learning Framework For Sample-efficient Reinforcement Learning (2022)
  Chenyu Sun, Hangwei Qian, Chunyan Miao
  7.16
• Large Language Model-enhanced Reinforcement Learning for Low-Altitude Economy Networking (2025)
  Lingyi Cai et al.
  7.13
• Reasoning Language Models: A Blueprint (2025)
  Maciej Besta et al.
  7.12
• Your Reward Function for RL is Your Best PRM for Search: Unifying RL and Search-Based TTS (2025)
  Can Jin et al.
  7.11
• Planning in entropy-regularized Markov decision processes and games (2026)
  Jean-Bastien Grill et al.
  7.08
• Incentivizing Agentic Reasoning in LLM Judges via Tool-Integrated Reinforcement Learning (2025)
  Ran Xu et al.
  7.02
• Options Of Interest: Temporal Abstraction With Interest Functions (2020)
  Khimya Khetarpal, Martin Klissarov, Maxime Chevalier-Boisvert, et al.
  6.77
• What Matters for Batch Online Reinforcement Learning in Robotics? (2025)
  Perry Dong et al.
  6.75
• Mitigating Reward Over-Optimization in RLHF via Behavior-Supported Regularization (2025)
  Juntao Dai et al.
  6.63
• Outcome-based Reinforcement Learning to Predict the Future (2025)
  Benjamin Turtel et al.
  6.53
• ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning (2024)
  Kun Wu et al.
  6.52
• Reinforcement Learning From State and Temporal Differences (2025)
  Lex Weaver and Jonathan Baxter
  6.40
• Deep Multi-Objective Reinforcement Learning for Utility-Based Infrastructural Maintenance Optimization (2024)
  Jesse van Remmerden et al.
  6.39
• Transformer-Empowered Actor-Critic Reinforcement Learning for Sequence-Aware Service Function Chain Partitioning (2025)
  Cyril Shih-Huan Hsu et al.
  6.36
• Search-o1: Agentic Search-Enhanced Large Reasoning Models (2025)
  Xiaoxi Li et al.
  6.30
• Post Reinforcement Learning Inference (2023)
  Vasilis Syrgkanis et al.
  6.28
• Intelligent Task Scheduling for Microservices via A3C-Based Reinforcement Learning (2025)
  Yang Wang et al.
  6.28
• $O(1/k)$ Finite-Time Bound for Non-Linear Two-Time-Scale Stochastic Approximation (2025)
  Siddharth Chandak
  6.23
• Coordinating Ride-Pooling with Public Transit using Reward-Guided Conservative Q-Learning: An Offline Training and Online Fine-Tuning Reinforcement Learning Framework (2025)
  Yulong Hu et al.
  6.06
• Human-in-the-Loop AI for HVAC Management Enhancing Comfort and Energy Efficiency (2025)
  Xinyu Liang et al.
  6.01
• Retrospex: Language Agent Meets Offline Reinforcement Learning Critic (2025)
  Yufei Xiang and Yiqun Shen and Yeqin Zhang and Cam-Tu Nguyen
  6.01
• DEAS: DEtached value learning with Action Sequence for Scalable Offline RL (2025)
  Changyeon Kim et al.
  5.98
• Impact of Collective Behaviors of Autonomous Vehicles on Urban Traffic Dynamics: A Multi-Agent Reinforcement Learning Approach (2025)
  Ahmet Onur Akman et al.
  5.93
• A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control (2025)
  Zilin Kang et al.
  5.82
• NormGuard: Reward-Preserving Norm Constraints in Flow-Matching Reinforcement Learning (2026)
  Tianlin Pan et al.
  5.82
• Evaluating Scenario-based Decision-making for Interactive Autonomous Driving Using Rational Criteria: A Survey (2025)
  Zhen Tian et al.
  5.79
• Part II: ROLL Flash -- Accelerating RLVR and Agentic Training with Asynchrony (2025)
  Han Lu et al.
  5.63
• SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning (2025)
  Yuqian Fu et al.
  5.49