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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Policy Complexity, Reaction Time, and Bounded Rationality in Reinforcement Learning
Sep 23, 2026James Wu, Chris R. SimsComputational Cognitive Modeling Reinforcement Learning
Department of Computer Science Rensselaer Polytechnic Institute Troy, NY, 12180 · Department of Cognitive Science Rensselaer Polytechnic Institute Troy, NY, 12180
RLVR landscapes for iterated multiplications can be benign: Insights from spin-glass theory
Sep 23, 2026Noa Rubin, Zohar RingelReinforcement Learning Maximum Entropy RL
Racah Institute of Physics The Hebrew University of Jerusalem
RL Starts before RL: On Policy Distillation for Better Reinforcement Learning
Sep 23, 2026Shuai Dong, Yongfu Zhu, Yuqi Xu +26Reinforcement Learning RL for Language Model Reasoning
Fudan University · SII · Peking University +2
Curriculum Learning with GNN-based Reinforcement Learning for Job Shop Scheduling
Sep 23, 2026Jayakrishnan K. Vasudevan, Jonathan Hoss, Noah KlarmannReinforcement Learning Curriculum RL
Rosenheim University of Applied Sciences · Dept. of Industrial Engineering Rosenheim University of Applied Sciences Rosenheim, Germany
Auditability Is Not One Property: Rule Overlap, Behavioural Agreement, and Composition in Reinforcement Learning
Sep 23, 2026Liu Hung MingReinforcement Learning AI Agent Auditing
PARRAWA AI
Robust Adversarial Reinforcement Learning with Risk Sensitivity and Critic Consistency Regularization
Sep 23, 2026Jiaxi Wu, Tiantian Zhang, Yuxing Wang +2Reinforcement Learning Risk-Sensitive RL
Shenzhen International Graduate School, Tsinghua University Shenzhen, China
DCRL: Decoupling and Coupling Reinforcement Learning via Policy-Reward Manifold Alignment
Sep 23, 2026Henan Sun, Zehua Li, Haitao Hu +4Reinforcement Learning RL for Language Model Reasoning
The Hong Kong University of Science and Technology (Guangzhou) · Huawei Noah’s Ark Lab · Tencent HY +1
EBRL: Asynchronous Embodied RL by Multi-Grained Resource Management
Sep 23, 2026Liang Mi, Weijun Wang, Bowen Gao +12Asynchronous RL Reinforcement Learning
Nanjing University · Institute for AI Industry Research (AIR), Tsinghua University · Nanjing University of Posts and Telecommunications +1
Verifiable Hidden Dynamics Play: Generating Agentic RL Environments from Solved Mechanisms
Sep 23, 2026Xinjie Shen, Wei Fan, Xudong Guo +5Agentic RL Reinforcement Learning
Georgia Institute of Technology · Alibaba Token Foundry, Alibaba Group
Turning Safety into Competence: Minimally Exploitable Robot Policies via Safety-Filtered Reinforcement Learning
Sep 23, 2026Ruihan Wu, Rui Yang, Donggeon David Oh +2Sequential MARL RL for Robotics
Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA · Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA
WTF?! Simulation-Free Reinforcement Learning with Wasserstein-Tilted Flow Maps
Sep 22, 2026Abbas Mammadov, Jerry Y. Huang, Justin Lin +5Reinforcement Learning Generative Flow Networks
University of Oxford · Carnegie Mellon University
PACT: From Credit Assignment to Critic Alignment
Sep 22, 2026Jiayan Fu, Hang Xu, Yong Zhang +4Reinforcement Learning Token-Level Credit Assignment
AllSpark Team
Rollout Efficiency in Reinforcement Learning for Reasoning Large Language Models: A Taxonomy and Future Directions
Sep 21, 2026Niloofar Gholipour, Marcos Assuncao, Gursimran Singh +8LLM Inference Efficiency Reinforcement Learning
École de technologie supérieure, Univ. of Québec, Canada · Huawei Technologies, Canada · The Univ. of Melbourne, Australia +1
Correcting Within-Group Self-Selection Bias in Prioritized Replay
Sep 21, 2026Oscar Miró López-Feliu, Herke van HoofReinforcement Learning Sample-Efficient RL
University of Amsterdam
Learning to Plan in Human-Robot Collaboration: Multimodal Reinforcement Learning for Adaptive Interaction
Sep 21, 2026Afagh Mehri Shervedani, Siyu Li, Natawut Monaikul +3Reinforcement Learning Robotic RL
University of Illinois Chicago, Chicago, IL 60607 USA · California State University Channel Islands
Critical-State RL: Diagnosing Trainable States for Multi-Turn Tool Use
Sep 21, 2026Zixiang Chen, Wenting Zhao, Zhepeng Cen +9Reinforcement Learning Credit Assignment in RL
Salesforce AI Research
Reinforcement Learning in Operational Research: A Technical Review and Practical Roadmap
Sep 21, 2026Yahan Lu, Dongyang Xia, Nursen Aydin +1Reinforcement Learning Sequential Decision Making
Department of Transport & Planning, Delft University of Technology, The Netherlands · Warwick Business School, University of Warwick, UK
Lifted Bellman Linear Programming for Offline Reinforcement Learning
Sep 21, 2026Hyukjun Yang, Jongchan Park, Narim Jeong +1Reinforcement Learning Offline RL
Korea Advanced Institute of Science and Technology(KAIST) · NAVER LABS
Information-Time Proximal Policy Optimization
Sep 21, 2026Yongcheng Zeng, Xinyu Cui, Yan Song +9Reinforcement Learning RL for Language Model Reasoning
Institute of Automation, Chinese Academy of Sciences, China · School of Artificial Intelligence, University of Chinese Academy of Sciences, China · University College London +5
Performance-Preserving Online Adaptation in Social Navigation via Diffusion Steering
Sep 21, 2026Haruto Nagahisa, Kohei Matsumoto, Yuki Hyodo +1Robot Policy Adaptation Reinforcement Learning
Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan · Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan
MimicAgent: Quadruped Skills via Prompt-to-Trajectory Generation
Sep 21, 2026Lucky Kant Nayak, Narayanan Palghat Parameswaran, Neehar Peri +1Reinforcement Learning Trajectory Generation
Carnegie Mellon University
Statistical Convergence of Transformer Encoder-Accelerated Robust Reinforcement Learning
Sep 20, 2026Suman Banerjee, Hiroyasu TsukamotoReinforcement Learning Transformer-Based RL
Department of Aerospace Engineering, The Grainger College of Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA
From Pretraining to Proficiency: Real-World Subtask RL for Long-Horizon Manipulation with Minimal Human Intervention
Sep 18, 2026Sichang Su, Benjamin Yang, Zhiyun Deng +4Robot Policy Adaptation Reinforcement Learning
UT Austin · Autel US · UC Berkeley
OmniVChat: Synthesizing, Benchmarking, and Training for Native Audio-Visual Dialogue
Sep 18, 2026Haolin He, Yunfei Chu, Qi Chen +15Reinforcement Learning Synthetic Data Generation
The Chinese University of Hong Kong · Alibaba Token Hub, Alibaba Group · Shanghai Jiao Tong University +2
Don't Mask the Environment: Observation Supervision Changes How Agents Explore Under RL
Sep 17, 2026Juzheng Zhang, Disha Makhija, Manoj Ghuhan Arivazhagan +2Supervised Fine-Tuning Reinforcement Learning
University of Maryland · AWS AI Labs
A Mathematical Model of Motivated Emotional Mind - Cognitive Embodied System
Sep 17, 2026Wiesław L. Galus, Janusz A. StarzykCognitive Architectures for AI Agents Computational Cognitive Modeling
Model-based Bootstrap for Offline Policy Evaluation in Tabular Reinforcement Learning
Sep 17, 2026Weiwei Wang, Yuqiang Li, Xianyi Wu +1Reinforcement Learning Off-Policy Evaluation
Department of Statistics and Data Science, Southern University of Science and Technology · School of Statistics & KLATASDS-MOE, East China Normal University · School of Artificial Intelligence, The Chinese University of Hong Kong, Shenzhen +1
GR2PO: Group Relative Return Policy Optimization for Continuous Robot Control
Sep 17, 2026Pengqin Wang, Qiming Zhang, Shaojie Shen +1Reinforcement Learning Group Relative Policy Optimization
Robotics and Autonomous Systems Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · Division of Emerging Interdisciplinary Areas, the Hong Kong University of Science and Technology, Hong Kong SAR, China · Intelligent Transportation Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China +1
EmbodiedMind: Adaptive Data Curation and Prefix-Tree Reinforcement Learning for Efficient Embodied Intelligence
Sep 17, 2026Feifan Wang, Zongbing Zhang, Yu Zhang +8Reinforcement Learning Robot Foundation Models
ZTE Corporation, Shenzhen, China
RLLBC-Lib: An Educational Code Library for Reinforcement Learning and Learning-Based Control
Sep 16, 2026Bernd Frauenknecht, Emma Cramer, Artur Eisele +9RL Control Reinforcement Learning
Institute for Data Science in Mechanical Engineering (DSME) RWTH Aachen University 52062 Aachen, Germany

Sep 23, 2026, cs.LGJ/K move · Enter open · S save

Policy Complexity, Reaction Time, and Bounded Rationality in Reinforcement Learning

James Wu, Chris R. Sims

Department of Computer Science Rensselaer Polytechnic Institute Troy, NY, 12180 · Department of Cognitive Science Rensselaer Polytechnic Institute Troy, NY, 12180

Computational Cognitive Modeling Reinforcement Learning Temporal-Difference Learning

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Biological agents do not learn under conditions of unlimited computation. For humans, learning and choice are shaped by constraints on perception, attention, and working memory, which limit how much state information guides behavior and therefore bound policy complexity. Standard reinforcement learning models typically optimize reward without explicitly representing these internal costs, making them less suitable as models of biological intelligence. We derive MI-SARSA, an on-policy temporal-difference algorithm that incorporates mutual-information regularization through a learned marginal action prior and a penalty on state-specific deviations from that prior. This yields a sequential learning model in which state information is used selectively when its expected return benefit justifies the added informational cost. Critically, the same state-specific information cost that governs policy compression also generates trial-level predictions for reaction time, distinguishing MI-SARSA from most reinforcement learning models, which predict choices or returns but not latency. Empirically, MI-SARSA produces a reward-complexity tradeoff, and stronger information penalties produce simpler policies with lower control costs and faster reaction times. Under environment shift, increasing regularization reduces post-switch performance degradation but also lowers asymptotic return, revealing a robustness-capacity tradeoff. Together, these results position MI-SARSA as a model of bounded sequential learning under cognitive constraints.

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Latest papers 1,138