Reinforcement Learning

Also known as RL

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H-GRPO: Permutation-Invariant Reinforcement Learning for Grounded Visual Reasoning
Jun 29, 2026Eric Peh, Debaditya Roy, Basura FernandoReinforcement Learning Visual Reasoning
Institute of High-Performance Computing, Agency for Science, Technology and Research, Singapore · Centre for Frontier AI Research, Agency for Science, Technology and Research, Singapore · Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, India +1
Trust Your Instincts: Confidence-Driven Test-Time RL for Vision-Language-Action Models
Jun 29, 2026Siyao Chen, Jiakang Yuan, Jiaxin Wang +1Intrinsic Reward Methods Reinforcement Learning
College of Future Information Technology, Fudan University, Shanghai, China · Shanghai Innovation Institute, Shanghai, China · MoShen Intelligence, Shanghai, China
RoAd-RL: A Unified Library and Benchmark for Robust Adversarial Reinforcement Learning
Jun 29, 2026Adithya Mohan, Daniel Kriegl, Torsten SchönRL Benchmarks Reinforcement Learning
AImotion Bavaria · Technische Hochschule Ingolstadt · Ingolstadt, Germany
Dual-Flow Reinforcement Learning with State-Aware Exploration
Jun 29, 2026Qijun Li, Zheng Fu, Qi Song +4Distributional RL Reinforcement Learning
School of Vehicle and Mobility and the State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing 100084, China
PS-PPO: Prefix-Sampling PPO for Critic-Free RLHF
Jun 29, 2026Doo Hwan Hwang, Kee-Eung KimReinforcement Learning RL for Language Model Reasoning
Kim Jaechul Graduate School of AI, KAIST, Daejeon, South Korea.
The Mirage of Optimizing Training Policies: Monotonic Inference Policies as the Real Objective for LLM Reinforcement Learning
Jun 28, 2026Jing Liang, Hongyao Tang, Yi Ma +9Reinforcement Learning RL for Language Model Reasoning
1Tianjin · 2Alibaba
Reinforcement Learning in Super Mario Bros: Curriculum, Pedagogy, and Optimal Level Design in World 1-1
Jun 28, 2026Jesse Ponnock, Lucas HoReinforcement Learning Curriculum RL
Johns Hopkins University
Process Advantage Signal Shaping: A Paradigm-Agnostic Middleware for Process-Supervised RL in LLM Reasoners
Jun 28, 2026Chao Wang, Hongtao Tian, Tao Yang +3Reinforcement Learning Process Reward Models
Tsinghua University · Work done while interning at Tencent. · WeChat, Tencent
CORE Planner: Contextual-memory Oriented Reinforcement-learning in Unknown Environments for Robot Navigation
Jun 28, 2026Jintao Kong, Zhihao Zhang, Weihuang Chen +4Reinforcement Learning Robot Navigation
State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University
Modification-Considering Value Learning for Reward Hacking Mitigation in RL
Jun 27, 2026Evgenii Opryshko, Umangi Jain, Igor GilitschenskiReward Hacking Reinforcement Learning
University of Toronto · Vector Institute
BV-Blend: Uncertainty-Weighted Historical Baselines for Stable Critic-Free RL with Verifiable Rewards
Jun 27, 2026Yupeng Chang, Yuan Wu, Yi ChangReinforcement Learning RL for Language Model Reasoning
School of Artificial Intelligence, Jilin University · 2Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, MOE, China · 3International Center of Future Science, Jilin University
Neuromorphic Energy-Aware Learning for Adaptive Deep Brain Stimulation
Jun 26, 2026Binh Nguyen, Colleen Josephson, Mircea Teodorescu +2Reinforcement Learning Neuromorphic Computing
Dept. of Electrical and Computer Engineering, University of California, Santa Cruz, CA, USA · Dept. of Bioengineering, University of California, San Diego, La Jolla, CA, USA
Tandem Reinforcement Learning with Verifiable Rewards
Jun 26, 2026Difan Jiao, Raghav Singhal, Robert West +1Reinforcement Learning RL for Language Model Reasoning
University of Toronto · EPFL
Regularized Reward-Punishment Reinforcement Learning
Jun 26, 2026Jiexin Wang, Eiji UchibeReinforcement Learning KL-Regularized RL
Dept. of Brain Robot Interface, ATR Computational Neuroscience Laboratories, 2-2-2, Hikaridai, Seikacho, Soraku-gun, Kyoto, 619-0288, Japan
Position: RL Researchers Need to Distinguish Between Solving Simulators and Using Simulators as a Proxy
Jun 26, 2026Matthew Vandergrift, Esraa Elelimy, Martha WhiteRL Benchmarks Reinforcement Learning
Department of Computing Science, University of Alberta · Alberta Machine Intelligence Institute (Amii) · Canada CIFAR AI Chair
Retroactive Advantage Correction: Closed-Form V-Trace Bias Correction for Delay-Aware RLHF
Jun 25, 2026Arnav RajAsynchronous RL Reinforcement Learning
Department of Computer Science and Engineering, Indian Institute of Technology Delhi, New Delhi, India.
Reinforcement Learning without Ground-Truth Solutions can Improve LLMs
Jun 25, 2026Yingyu Lin, Qiyue Gao, Nikki Lijing Kuang +6RL for Code Generation Reinforcement Learning
University of California, San Diego · Snowflake AI Research
RolloutPipe: Overlapping Pipelined Rollout and Training in Disaggregated On-Policy LLM Reinforcement Learning
Jun 25, 2026Rongjian Chen, Jianmin Hu, Kejiang Ye +1Reinforcement Learning RL for Language Model Reasoning
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China · University of Chinese Academy of Sciences, Beijing, China · Southern University of Science and Technology, Shenzhen, China
GEOALIGN: Geometric Rollout Curation for Robust LLM Reinforcement Learning
Jun 25, 2026Ting Zhou, Zhenqing Ling, Yiyang Zhao +2RL for Language Models LLM Alignment
Sun Yat-Sen University, Guangzhou, China · Alibaba Group, Hangzhou, China · Guangdong Tobacco Guangzhou Co., Ltd., Guangzhou, China
Revisiting Action Factorization for Complex Action Spaces
Jun 25, 2026Timothy Flavin, Sandip SenReinforcement Learning Actor-Critic Methods
The University of Tulsa, Tulsa, Oklahoma, USA
VoiceTTA: Enhancing Zero-Shot Text-to-Speech via Reinforcement Learning-Based Test-Time Adaptation
Jun 25, 2026Tianxin Xie, Chenxing Li, Dong Yu +1Test-Time Adaptation Reinforcement Learning
The Hong Kong University of Science and Technology (Guangzhou) · Tencent
Sample-efficient Transfer Reinforcement Learning via Adaptive Reward Shaping and Policy-Ratio Reweighting Strategy
Jun 25, 2026Wenjie Huang, Yang Li, Jingjia Teng +6Reinforcement Learning Reward Shaping
State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China · Key Laboratory of Intelligent Air-Ground Cooperative Control for Universities in Chongqing, School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China · Xi’an Institute of High-Tech, Xi’an 710025, China +1
Mean-Field PhiBE: Continuous-Time Mean-Field Reinforcement Learning from Discrete-Time Data
Jun 25, 2026Erhan Bayraktar, Martin Hernandez, Qinxin Yan +1Reinforcement Learning Stochastic Optimal Control
Department of Mathematics, University of Michigan, Ann Arbor, MI, USA. · Department of Statistics and Data Science, University of California, Los Angeles, CA, USA. · Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ, USA.
Finding the Time to Think: Learning Planning Budgets in Real-Time RL
Jun 24, 2026Aneesh Muppidi, Firas Darwish, Dylan Cope +2Reinforcement Learning Policy Learning
British Open-ended Learning and Discovery Lab (BOLD), University of Oxford · Department of Statistics, University of Oxford · VGG, University of Oxford
MPC-Injection: Biasing Off-Policy Locomotion RL Toward Controller-Induced Behavior Basins
Jun 24, 2026Roy Xing, Seyoung Ree, Brian PlancherReinforcement Learning RL for Legged Locomotion
Dartmouth College · Harvard University
Mesh-RL: Coupled subgrid reinforcement learning
Jun 24, 2026Behnam Gheshlaghi, Bahador Rashidi, Shahin AtakishiyevReinforcement Learning Credit Assignment in RL
Independent Researcher · University of Alberta
Reinforcement Learning for Software Vulnerability Analysis: A Systematic Review with Emphasis on C/C++ Source Code and Static Analysis
Jun 24, 2026Bruno Caro-Vásquez, Carola Figueroa-Flores, Gastón MarquezReinforcement Learning Software Vulnerability Detection
Master’s Student in Computer Science, Universidad del Bío-Bío, Chile · Department of Computer Science and Information Technology, Universidad del Bío-Bío, Chile
OPERA: Aligning Open-Ended Reasoning via Objective Perplexity-based Reinforcement Learning
Jun 24, 2026Wenxuan Jiang, Zining Fan, Zijian Zhang +6Open-Ended Generation Intrinsic Reward Methods
The Hong Kong Polytechnic University · Peking University · Meituan Longcat Team +2
Low-Complexity Policy Tessellations in Structured Markov Decision Processes
Jun 24, 2026Fredy PokouMarkov Decision Processes Reinforcement Learning
CRIStAL · Inria, University of Lille, CNRS, Centrale Lille Villeneuve-d’Ascq, France
Implementation of reinforcement learning in chemical reaction networks: application to phototaxis as curiosity-driven exploration
Jun 24, 2026Ruyi Tang, Grégoire Sergeant-Perthuis, David ColliauxDynamical Systems Reinforcement Learning
LCQB-AG · 1CQSB, Sorbonne University, France · 2Sony CSL, France

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H-GRPO: Permutation-Invariant Reinforcement Learning for Grounded Visual Reasoning

Eric Peh, Debaditya Roy, Basura Fernando

Institute of High-Performance Computing, Agency for Science, Technology and Research, Singapore · Centre for Frontier AI Research, Agency for Science, Technology and Research, Singapore · Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, India+1

Reinforcement Learning Visual Reasoning VLM Interpretability Vision-Language Grounding VLM Reasoning Visually Grounded Reasoning+1

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Vision-Language Models (VLMs) often achieve high performance on benchmarks while remaining "black boxes", yet they remain prone to hallucination or rely on superficial shortcuts. In this work, we propose a framework designed to enhance both performance and interpretability through De-compositional Evidence Grounding. Unlike monolithic inference approaches, our approach forces the model to decompose a global query into a sequence of atomic sub-questions, each requiring an explicit sub-answer and critically a localized evidence bounding box. By grounding intermediate logical steps (e.g. identifying a container, analyzing liquid properties, and assessing environmental context) in specific visual regions, we construct a structured reasoning path that mirrors human-like deduction. This allows the final answer to emerge as a logical consequence of verified visual facts rather than a statistical guess.

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Latest papers 1,138