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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A Lecture Note on Offline RL and IRL, Part II: Foundations of Inverse Reinforcement Learning and Dynamic Discrete Choice Models
May 29, 2026Enoch Hyunwook KangReinforcement Learning Sequential Decision Making
University of Washington, Foster School of Business
Smaller Models are Natural Explorers for Policy-Level Diversity in GRPO
May 29, 2026Yiming Ren, Yiran Xu, Zicheng Lin +8Reinforcement Learning RL for Language Model Reasoning
Tsinghua University · Shanghai AI Laboratory · The Chinese University of Hong Kong +1
Efficient and Uncertainty-Aware Diffusion Framework for Offline-to-Online Reinforcement Learning
May 29, 2026Ha Manh Bui, Metod Jazbec, Eric Nalisnick +1Reinforcement Learning Diffusion Policy
Department of Computer Science, Johns Hopkins University, Baltimore, MD, U.S.A. · AMLab, University of Amsterdam, Amsterdam, Netherlands
Learning to Perceive the World Through Control: Empowerment-Based Representation Learning
May 28, 2026Mahsa Bastankhah, Sophie Broderick, Benjamin EysenbachRepresentation Learning Reinforcement Learning
Princeton University, USA
ZAPS-DA: Zero-Phase Action Policy Smoothing with Decoupled Actor for Continuous Control in Reinforcement Learning
May 28, 2026Faiq ShamassRL Control Reinforcement Learning
San Diego, CA, USA
Uncertainty-Aware and Temporally Regulated Expert Advice in Reinforcement Learning for Autonomous Driving
May 28, 2026Ahmed Abouelazm, Felix Klingebiel, Philip Schörner +1Reinforcement Learning Risk-Sensitive RL
FZI Research Center for Information Technology, Germany · Karlsruhe Institute of Technology, Germany
Physics-informed Goal-Conditioned Reinforcement Learning under Hybrid Contact Dynamics
May 28, 2026Vittorio Giammarino, Anastasios Manganaris, Ahmed H. QureshiReinforcement Learning Contact-Rich Robotic Manipulation
Department of Computer Science Purdue University
Stable-Layers: Fine-Tuning Image Layer Decomposition Models with VLM-Scored Reinforcement Learning
May 28, 2026Ciara Rowles, Reshinth Adithyan, Nikhil Pinnaparaju +2Reinforcement Learning
Stability AI
Reinforcement Learning with Robust Rubric Rewards
May 28, 2026Ya-Qi Yu, Hao Wang, Fangyu Hong +15Reinforcement Learning Rubric-Based RL
Huawei Technologies Co., Ltd.
TriSearch: Learning to Optimize Triangulations via Bistellar Flips
May 28, 2026Yiran Wang, Guido MontúfarReinforcement Learning RL for Combinatorial Optimization
UCLA · MPI MiS
On Distributional Reinforcement Learning in Chaotic Dynamical Systems
May 28, 2026James Rudd-Jones, Mirco Musolesi, María Pérez-OrtizDistributional RL Reinforcement Learning
Centre for Artificial Intelligence Department of Computer Science University College London London, UK · Department of Computer Science and Engineering, University of Bologna Bologna, Italy
RL2ML: Finite-Rollout Surrogate Objectives from Reinforcement Learning to Maximum Likelihood
May 28, 2026Yifu ZhengRL for Language Models Policy Gradient
University of Southern California
LLM-Guided Future Hypotheses for Horizon-Aware Exploration in Multi-Step Robot Manipulation
May 28, 2026Mohammad Khoshnazar, Andrew Melnik, Michael BeetzRL for Robotics Reinforcement Learning
Institute of Artificial Intelligence, University of Bremen, Germany.
ESPO: Early-Stopping Proximal Policy Optimization
May 28, 2026Zihang Li, Rui Zhou, Yingcheng Shi +8Early Stopping Reinforcement Learning
1Tongyi Lab , Alibaba Group · 2Peking University
EvoRubric: Self-Evolving Rubric-Driven RL for Open-Ended Generation
May 28, 2026Xin Guan, Xiaomeng Hu, Shen Huang +6Open-Ended Generation RL for Language Models
Tongyi Lab, Alibaba Group
Momentum Based Reward Design for Low Emission Traffic Signal Control
May 28, 2026Chinmay Mundane, Amith Manoharan, Arun Kumar SinghReinforcement Learning Reward Shaping
Institute of Technology, University of Tartu
DeepTool: Scaling Interleaved Deliberation in Tool-Integrated Reasoning via Process-Supervised Reinforcement Learning
May 28, 2026Yang He, Xiao Ding, Bibo Cai +5Reinforcement Learning RL for Language Model Reasoning
Research Center for Social Computing and Interactive Robotics, Harbin Institute of Technology, China
Prompt-Level Reward Specifications for Open-Ended Post-Training
May 28, 2026Zijun Weng, Xiaohui Hu, Shuangyong Song +3Open-Ended Generation Reward Modeling
1Fudan University · 2Xingchen AGI Lab, China Telecom Artificial Intelligence Technology (Beijing) Co., Ltd.
PRO-CUA: Process-Reward Optimization for Computer Use Agents
May 27, 2026Yifei He, Rui Yang, Hao Bai +2Reinforcement Learning Process Reward Models
University of Illinois Urbana-Champaign
Bridging the Sim-to-Real Gap in Reinforcement Learning-Based Industrial Dispatching through Execution Semantics
May 27, 2026Jonathan Hoss, Noah KlarmannReinforcement Learning Sim-to-Real Transfer
Department of Industrial Engineering
Return-to-Go Is More Than a Number: Q-Guided Alignment for Return-Conditioned Supervised Learning
May 27, 2026Yuxiao Yang, Weitong ZhangReinforcement Learning Decision Transformer
University of North Carolina at Chapel Hill
Label-Free Reinforcement Learning via Cross-Model Entropy
May 27, 2026Matt Gorbett, Hossein ShiraziReinforcement Learning Instruction Following
Independent Researcher · San Diego State University
Optimal Data Acquisition for Reinforcement Learning: A Large Deviations Perspective
May 27, 2026Mingjie Hu, Jian-Qiang Hu, Enlu ZhouReinforcement Learning Sample-Efficient RL
School of Management, Fudan University · H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
Soft-SVeRL: Self-Verified Reinforcement Learning with Soft Rewards
May 27, 2026Saurabh Dash, Pierre Clavier, John Dang +4Reinforcement Learning Instruction Following
Cohere Labs · Cohere · Adaption Labs +1
Mechanistically Interpreting the Role of Sample Difficulty in RLVR for LLMs
May 27, 2026Yue Cheng, Jiajun Zhang, Xiaohui Gao +3Reinforcement Learning RL for Language Model Reasoning
Beijing Jiaotong University · AntGroup · Northwestern Polytechnical University +2
Variance-Adaptive Optimal Algorithm for Reinforcement Learning with Multinomial Logit Function Approximation
May 27, 2026Wonyoung Kim, Min-Hwan Oh, Garud Iyengar +1Regret Minimization in RL Reinforcement Learning
Chung-Ang University · Seoul National University · Columbia University
Commit to the Bit: Reactive Reinforcement Learning Done Right
May 27, 2026Onno Eberhard, Claire Vernade, Michael MuehlebachReinforcement Learning Partially Observable RL
Max Planck Institute for Intelligent Systems, Tübingen, Germany · University of Tübingen · University of Technology Nuremberg.
IRDS: Interpretable RLVR Data Selection via Verifier-Coupled Sparse Autoencoder Coverage
May 27, 2026Yuhan Li, Mingxu Zhang, Dazhong Shen +1Reinforcement Learning RL for Language Model Reasoning
The Hong Kong University of Science and Technology (Guangzhou) · Nanjing University of Aeronautics and Astronautics · The 63rd Research Institute, National University of Defense Technology, Nanjing
Off-Policy Learning to Reason Works Because It Is More Pessimistic Than You Think
May 27, 2026Otmane Sakhi, Aleksei Arzhantsev, Imad Aouali +1Reinforcement Learning RL for Language Model Reasoning
Criteo AI Lab · Criteo AI Lab, CREST IP Paris
Cyclical Entropy Eruption: Entropy Dynamics in Agent Reinforcement Learning
May 27, 2026Wendi Li, Shawn Im, Sharon LiAgentic RL Reinforcement Learning
Department of Computer Sciences, University of Wisconsin–Madison

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A Lecture Note on Offline RL and IRL, Part II: Foundations of Inverse Reinforcement Learning and Dynamic Discrete Choice Models

Enoch Hyunwook Kang

University of Washington, Foster School of Business

Reinforcement Learning Sequential Decision Making Offline RL

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In the forward reinforcement-learning problem, the reward is fixed and known; the learner is asked to find a good policy or value function. Here we turn the question around. Given offline data generated by an expert, can we recover the reward the expert was optimizing? This is the inverse reinforcement learning problem, and remarkably, two communities, structural econometricians studying dynamic discrete choice (DDC) and machine learners studying entropy-regularized IRL, have been working on exactly the same probabilistic model under different names. We begin by proving their equivalence. We then develop the classical identification result of Magnac and Thesmar and the classical computational paradigms that grew out of it: Rust's nested fixed-point algorithm, the conditional-choice-probability approach of Hotz and Miller, and the two temporal-difference approaches of Adusumilli and Eckardt: linear semi-gradient TD and approximate value iteration. Each route has its limits: dimensionality, transition-kernel estimation, the deadly triad, or projected fixed-point bias. We then walk through the modern ML/IRL strand: adversarial IRL, occupancy matching, IQ-Learn, and offline ML-IRL, deriving each method's actual objective and stating precisely what it does and does not identify. We close with the empirical-risk-minimization framework of Kang et al., which yields a gradient-based estimator for offline IRL/DDC.

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