Reinforcement Learning

Also known as RL

Follow this topic Ask about this topic

Momentum

144 papers in the last four weeks, up 243% on the four weeks before. 1.4% of all new papers.

Jul 13Week of Sep 28

Latest papers 1,138

All topics

Reward Transfer from Inverse Reinforcement Learning: A Coupled Minimax Approach
May 27, 2026Guang-Yuan Hao, Lars van der Laan, Aurélien Bibaut +1Reinforcement Learning Minimax Estimation
Cornell Tech, Cornell University · Netflix Research · Department of Statistics, University of Washington
Restoring the Sweet Spot: Pass-Rate Weighted Self-Distillation for LLM Reasoning
May 26, 2026Zehao Liu, Yuanpu Cao, Jinghui Chen +1Reinforcement Learning On-Policy Self-Distillation
College of Information Sciences and Technology Pennsylvania State University
Bayesian Deployment Approval for Learned Landing Controllers under Finite Rollout Validation
May 26, 2026Fei Jiang, Lei YangRL Control Reinforcement Learning
Independent Researcher, Seattle, WA, USA.
Transferable Reinforcement Learning via Probabilistic Latent Embeddings and Dynamic Policy Adaptation for Sim-to-Real Deployment
May 26, 2026Gengyue Han, Yiheng FengReinforcement Learning Sim-to-Real Transfer
Lyles School of Civil and Construction Engineering, Purdue University, West Lafayette, USA · Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, USA
Accelerating Reinforcement Learning Training Using Simulation Surrogate Models
May 26, 2026Mohammadmahdi Ghasemloo, David J. Eckman, Yaxian LiSurrogate Modeling Reinforcement Learning
Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA · Intuit AI, Mountain View, CA, USA
Trust Region Q Adjoint Matching
May 26, 2026Yonghoon Dong, Kyungmin Lee, Changyeon Kim +2Reinforcement Learning KL-Regularized RL
KAIST AI · Seoul National University · RLWRLD
SQARL: A Size-Agnostic Reinforcement Learning approach for Circuit Allocation in Distributed Quantum Architectures
May 26, 2026Víctor Carballo, Júlia López-Closa, Mario MartinReinforcement Learning
Computer Science Department, Universitat Polit`ecnica de Catalunya - BarcelonaTech (UPC) · High Performance Artificial Intelligence group, Barcelona Supercomputing Center.
Darwin Mobile Agent: A Roadmap for Self-Evolution
May 26, 2026Daniel Beechey, Derek Yuen, Jianheng Liu +5Reinforcement Learning RL for GUI Agents
1Huawei Noah’s Ark Lab · University College London
Probabilistic Recurrent Intention Switching Model
May 26, 2026Wenyuan Sheng, Hao Zhu, Joschka BoedeckerReinforcement Learning
Department of Computer Science, University of Freiburg · IMBIT//BrainLinks-BrainTools
REVERSE: Reinforcing Evidence Verification and Search for Agentic Image geo-localization
May 26, 2026Yong Li, Furong Jia, Dacheng Yin +4Retrieval-Augmented Generation Reinforcement Learning
Peking University · The Hong Kong University of Science and Technology · WeChat Vision, Tencent Inc
Towards Generalization-Oriented Models for Vehicle Routing Problems with Mixture-of-Experts
May 26, 2026Changhao Miao, Yuntian Zhang, Tongyu Wu +2Reinforcement Learning Adaptive Model Routing
State Key Laboratory of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing 100081, China · Department of Computer, Control and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, Rome, 00185, Italy · School of AI, Beijing Institute of Technology, Beijing 100081, China
MedVol-R1: Reward-Driven Evidence Grounding for Volumetric Reasoning Segmentation
May 26, 2026Zichun Wang, Hairong Shi, Bingzheng Wei +2Image Segmentation Reinforcement Learning
School of Biological Science and Medical Engineering, Beihang University, Beijing, China · Center for Information and Computer Science, School of Science for Open and Environmental Systems, Graduate School of Science and Technology, Keio University, 2026 Kanagawa, Japan · Bytedance Inc., China +1
Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks
May 26, 2026Nguyen Cong Luong, Shaohan Feng, Nguyen Duc Hai +10Reinforcement Learning Transformer-Based RL
Faculty of Computer Science, Phenikaa University, Hanoi 12116, Vietnam · School of Information and Electronic Engineering (Sussex Artificial Intelligence Institute), Zhejiang Gongshang University, Hangzhou 310018, China · Faculty of Artificial Intelligence and Data Science, Phenikaa University, Duong Noi, Hanoi 12116, Vietnam +4
Spend Your Rollouts Where It Counts: Rollout Allocation for Group-Based RL Post-Training
May 26, 2026Woojeong Kim, Ziyi Yang, Jing Nathan Yan +1Reinforcement Learning RL for Language Model Reasoning
Databricks · Cornell University
Heterogeneous AAV Logistics Task Allocation: A Reinforcement Learning Enhanced Overlapping Coalition Formation Game Approach
May 26, 2026Yuze Zhou, Jingliang Sun, Junzhi Li +3Reinforcement Learning Transformer-Based RL
Beijing Institute of Technology, Beijing 100081, China · Key Laboratory of Dynamics and Control of Flight Vehicle, Ministry of Education, Beijing 100081, China · Beijing Institute of Technology Chongqing Innovation Center, Chongqing, 401121, China +1
From Static Context to Calibrated Interactive RL: Mitigating Distribution Shift in Multi-turn Dialogue with Aligned Simulator
May 26, 2026Xiaohua Wang, Jiakang Yuan, Zisu Huang +5RL for Language Models Reinforcement Learning
Fudan University
Balancing Plasticity and Stability with Fast and Slow Successor Features
May 25, 2026Raymond Chua, Doina Precup, Blake RichardsStability-Plasticity Dilemma Non-Stationary RL
School of Computer Science, McGill University, Montr´eal, Canada · Mila - Quebec Artificial Intelligence Institute, Montr´eal, QC, Canada · CIFAR Learning in Machines and Brains +3
Peak-Then-Collapse and the Four Interface Channels of Knowledge-Graph Tool Use
May 25, 2026Tianda Sun, Dimitar KazakovTool-Use Evaluation Reinforcement Learning
University of York · Department of Computer Science · Heslington, York +1
Learning in Low-Dimensional Subspaces: Orthogonal Bottlenecks for Reinforcement Learning
May 25, 2026Aleksandar Todorov, Matthia SabatelliRepresentation Learning Reinforcement Learning
University of Groningen, Groningen, The Netherlands
When Self-Belief Misleads: Active Label Acquisition for Reinforcement Learning with Verifiable Rewards
May 25, 2026Li Wang, Xiaodong Lu, Xiaohan Wang +5Reinforcement Learning RL for Language Model Reasoning
Meituan · Beihang University · Nanyang Technological University
Latent Representation Alignment for Offline Goal-Conditioned Reinforcement Learning
May 25, 2026Hyungkyu Kang, Byeongchan Kim, Min-hwan OhReinforcement Learning Offline RL
Seoul National University, Seoul, South Korea.
Learning to Search and Searching to Learn for Generalization in Planning
May 25, 2026Michael Aichmüller, Yannik Hesse, Hector GeffnerReinforcement Learning Generalization in RL
Department of Machine Learning and Reasoning, RWTH Aachen University, Aachen, Germany.
Harmony in Diversity: Multi-domain Contrastive Policy Optimization for Large Reasoning Models
May 25, 2026Zongji Yu, Wenshui Luo, Yiliu Sun +5Reinforcement Learning RL for Language Model Reasoning
Shanghai Jiao Tong University · Shanghai AI Laboratory · Shandong University
Prior Policy Guided Dual-Agent Coordinated Manipulation Planning of Spacecraft-Manipulator System
May 25, 2026Yuhui Hu, Dong Zhou, Kaihong Ouyang +3Robotic Control Reinforcement Learning
School of Astronautics, Harbin Institute of Technology, Harbin, 150001, China · School of Automation, Chongqing University, Chongqing, 400044, China · School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China
Latent Q-Barrier Shielding for Safe In-Context Reinforcement Learning
May 24, 2026Minjae Kwon, Amir Moeini, Shangtong Zhang +1Reinforcement Learning Control Barrier Functions
University of Virginia
Quantifying Empirical Compute-Supervision Tradeoffs in RLVR
May 24, 2026Ryo Mitsuhashi, Patrick Chen, Isabelle Tseng +2Reinforcement Learning RL for Language Model Reasoning
1Princeton University
Evolutionary Enhanced Multi-Agent Reinforcement Learning for Cooperative Air Combat
May 24, 2026Chengwei Li, Junlin Liu, Yang GaoMulti-Agent Coordination Reinforcement Learning
Institute of Automation, Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences
A perspective on fluid mechanical environments for challenges in reinforcement learning
May 24, 2026Shruti Mishra, Michael Chang, Vamsi Spandan +1RL Control Non-Stationary RL
Sony AI · Cohere Labs Community · The Hebrew University of Jerusalem
Scaling up Energy-Aware Multi-Agent Reinforcement Learning for Mission-Oriented Drone Networks with Individual Reward
May 24, 2026Changling Li, Ying LiReinforcement Learning Credit Assignment in RL
Department of Computer Science, ETH Zurich, 8092, Zurich, Switzerland · Department of Computer Science, Colby College, 04901, Waterville, ME, USA
Global linear convergence of entropy-regularized softmax policy gradient beyond tabular MDPs
May 24, 2026Ziyue Chen, David Šiška, Lukasz SzpruchPolicy Gradient Reinforcement Learning

May 27, 2026, cs.LGJ/K move · Enter open · S save

Reward Transfer from Inverse Reinforcement Learning: A Coupled Minimax Approach

Guang-Yuan Hao, Lars van der Laan, Aurélien Bibaut, Nathan Kallus

Cornell Tech, Cornell University · Netflix Research · Department of Statistics, University of Washington

Reinforcement Learning Minimax Estimation

Open paper AskPDF arXiv

We study the transfer of rewards learned using inverse reinforcement learning from expert demonstrations in one environment to reinforcement learning in a new, different environment. This arises naturally when demonstrations are collected in a controlled environment. We formulate the problem as a joint system of Bellman equations across the source and target environments and develop minimax estimators for the target soft-

q

-function. Whereas a sequential solution approach first estimates the source reward and then plugs it into the target control problem, a coupled approach solves the source and target system of equations jointly. We show that, in contrast to the sequential approach, the coupled approach removes the first-order influence of source Bellman residual error. We characterize the local behavior of each approach, develop finite-sample soft-

q

-function error bounds, and prove regret guarantees for the resulting soft-control policy. An empirical investigation using a sepsis simulator validates the theoretical comparison.

Momentum

Related topics

Latest papers 1,138