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 Optimization in Hybrid Discrete-Continuous Action Spaces via Mixed Gradients
May 14, 2026Matias Alvo, Daniel Russo, Yash KanoriaRL Control Reinforcement Learning
Graduate School of Business Columbia University
CreFlow: Corrective Reflow for Sparse-Reward Embodied Video Diffusion RL
May 14, 2026Zhenyang Ni, Yijiang Li, Ruochen Jiao +7Video Diffusion Models Reinforcement Learning
Northwestern University · University of California, San Diego · University of Oxford
Diagnosing Training Inference Mismatch in LLM Reinforcement Learning via a Zero-Mismatch Reference
May 14, 2026Tianle Zhong, Neiwen Ling, Yifan Pi +5Reinforcement Learning LLM Inference
ByteDance · The University of Virginia
R2R2: Robust Representation for Intensive Experience Reuse via Redundancy Reduction in Self-Predictive Learning
May 13, 2026Sanghyeob Song, Donghyeok Lee, Jinsik Kim +1Reinforcement Learning Self-Supervised Learning
Interdisciplinary Program in Artificial Intelligence, Seoul National University · Samsung Electro-Mechanics, Republic of Korea · Department of Electrical and Computer Engineering, Seoul National University
Tight Sample Complexity Bounds for Entropic Best Policy Identification
May 13, 2026Amer Essakine, Claire VernadeReinforcement Learning Risk-Sensitive RL
ENS Paris Saclay · University of Technology Nuremberg
Self-Supervised On-Policy Reinforcement Learning via Contrastive Proximal Policy Optimisation
May 13, 2026Asim Osman, Sasha Abramowitz, Mark Bergh +13Reinforcement Learning Contrastive RL
InstaDeep, AIMS · InstaDeep, University of Stellenbosch
CA2: Code-Aware Agent for Automated Game Testing
May 13, 2026Valliappan Chidambaram Adaikkappan, Vincent Martineau, Joshua Romoff +1Automated Software Testing Reinforcement Learning
† McGill University · ⋄Mila Quebec AI Institute · ‡ Ubisoft
Q-Flow: Stable and Expressive Reinforcement Learning with Flow-Based Policy
May 13, 2026JaeHyeok Doo, Byeongguk Jeon, Seonghyeon Ye +2Reinforcement Learning Offline RL
1KAIST
D-VLA: A High-Concurrency Distributed Asynchronous Reinforcement Learning Framework for Vision-Language-Action Models
May 13, 2026Yucheng Guo, Yongjian Guo, Zhong Guan +9Reinforcement Learning Communication-Efficient Distributed Training
JDT AI Infra · Tsinghua University · Tianjin University +2
GAGPO: Generalized Advantage Grouped Policy Optimization
May 13, 2026Siyuan Zhu, Chao Yu, Rongxin Yang +4Reinforcement Learning Group Relative Policy Optimization
School of Computer Science and Engineering, Sun Yat-sen University · Meituan
Switching Successor Measures for Hierarchical Zero-shot Reinforcement Learning
May 13, 2026Stefan Stojanovic, Alexandre ProutiereRepresentation Learning Reinforcement Learning
KTH, Stockholm, Sweden · KTH, Digital Futures, Stockholm, Sweden
Offline Two-Player Zero-Sum Markov Games with KL Regularization
May 13, 2026Claire Chen, Yuheng Zhang, Xinyu Liu +3Nash Equilibrium Reinforcement Learning
1California Institute of Technology. · University of Illinois Urbana-Champaign. · University of Virginia. +2
Safe Continual Reinforcement Learning under Nonstationarity via Adaptive Safety Constraints
May 13, 2026Timofey TomashevskiyNon-Stationary RL Reinforcement Learning
McMaster Centre for Software Certification Department of Computing and Software McMaster University, Canada
AIS: Adaptive Importance Sampling for Quantized RL
May 13, 2026Jiajun Zhou, Wei Shao, Lingchao Zheng +2RL for Language Models Reinforcement Learning
Huawei · The University of Hong Kong
Quantifying Potential Observation Missingness in Inverse Reinforcement Learning
May 12, 2026Leo Benac, Abhishek Sharma, Alihan Huyuk +1Reinforcement Learning Healthcare
School of Engineering and Applied Sciences Harvard University
ODRPO: Ordinal Decompositions of Discrete Rewards for Robust Policy Optimization
May 12, 2026Nirmal Patel, Fei Wang, Inderjit S. DhillonRL for Language Models Reinforcement Learning
University of Texas at Austin · Google
Robust Instruction Compliance in Cooperative Multi-Agent Reinforcement Learning
May 12, 2026Wo Wei Lin, Ethan Rathbun, Enrico Marchesini +1Reinforcement Learning Robust RL
Department of Computer Sciences Northeastern University United States · Department of Computer Sciences Massachusetts Institute of Technology United States
Plan Before You Trade: Inference-Time Optimization for RL Trading Agents
May 12, 2026Eun Go, Rohan Deb, Arindam BanerjeeReinforcement Learning Inference-Time Optimization
Siebel School of Computing and Data Science University of Illinois Urbana-Champaign
Beyond GRPO and On-Policy Distillation: An Empirical Sparse-to-Dense Reward Principle for Language-Model Post-Training
May 12, 2026Yuanda Xu, Hejian Sang, Zhengze Zhou +3Reinforcement Learning RL for Language Model Reasoning
Reward Hacking in Rubric-Based Reinforcement Learning
May 12, 2026Anas Mahmoud, MohammadHossein Rezaei, Zihao Wang +3Reward Hacking Reinforcement Learning
Scale AI
Towards Affordable Energy: A Gymnasium Environment for Electric Utility Demand-Response Programs
May 12, 2026Jose E. Aguilar Escamilla, Lingdong Zhou, Xiangqi Zhu +1RL Benchmarks Reinforcement Learning
Department of Electrical Engineering and Computer Science, Oregon State University, USA
Discrete Flow Matching for Offline-to-Online Reinforcement Learning
May 12, 2026Fairoz Nower Khan, Nabuat Zaman Nahim, Peizhong JuFlow Matching Reinforcement Learning
Department of Computer Science, University of Kentucky
Transferable Delay-Aware Reinforcement Learning via Implicit Causal Graph Modeling
May 12, 2026Chenran Zhao, Dianxi Shi, Yaowen Zhang +2Causal Representation Learning Reinforcement Learning
College of Computer Science and Technology, National University of Defense Technology, Changsha, China · Institute of Military Transportation, Tianjin, China · Intelligent Game and Decision Lab (IGDL), Beijing, China
Delay-Empowered Causal Hierarchical Reinforcement Learning
May 12, 2026Chenran Zhao, Dianxi Shi, Haotian Wang +4Reinforcement Learning Hierarchical RL
College of Computer Science and Technology, National University of Defense Technology, Changsha, China · Intelligent Game and Decision Lab (IGDL), Beijing, China · School of Artificial Intelligence, Hebei University of Technology, Tianjin, China +1
Intrinsic Vicarious Conditioning for Deep Reinforcement Learning
May 12, 2026Rodney A Sanchez, Ferat Sahin, Alex Ororbia +1Reinforcement Learning Intrinsic Motivation
Department of Electrical and Microelectronic Engineering, Rochester Institute of Technology · Department of Computer Science, Rochester Institute of Technology
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning
May 12, 2026Asad Bakija, Florent De Geeter, Julien Brandoit +2Dynamical Systems Reinforcement Learning
Dept. of Electrical Engineering and Computer Science · University of Liège · Liège, Belgium
Missing Old Logits in Asynchronous Agentic RL: Semantic Mismatch and Repair Methods for Off-Policy Correction
May 12, 2026Zhong Guan, Yongjian Guo, Haoran Sun +5Asynchronous RL Reinforcement Learning
Tianjin University · Tsinghua University · JDT AI Infra +1
Learning Agentic Policy from Action Guidance
May 12, 2026Yuxiang Ji, Zengbin Wang, Yong Wang +6Agentic RL Reinforcement Learning
1Xiamen University · 2AMAP, Alibaba Group · Work done during internship at AMAP, Alibaba Group +1
Block-R1: Rethinking the Role of Block Size in Multi-domain Reinforcement Learning for Diffusion Large Language Models
May 12, 2026Yan Jiang, Ruihong Qiu, Zi HuangRL Benchmarks Reinforcement Learning
The University of Queensland
From Generic Correlation to Input-Specific Credit in On-Policy Self Distillation
May 12, 2026Guobin Shen, Lei Huang, Xiang Cheng +4Reinforcement Learning On-Policy Self-Distillation
Xiaohongshu Inc. · Institute of Automation, Chinese Academy of Sciences

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Policy Optimization in Hybrid Discrete-Continuous Action Spaces via Mixed Gradients

Matias Alvo, Daniel Russo, Yash Kanoria

Graduate School of Business Columbia University

RL Control Reinforcement Learning Policy Optimization

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We study reinforcement learning in hybrid discrete-continuous action spaces, such as settings where the discrete component selects a regime (or index) and the continuous component optimizes within it -- a structure common in robotics, control, and operations problems. Standard model-free policy gradient methods rely on score-function (SF) estimators and suffer from severe credit-assignment issues in high-dimensional settings, leading to poor gradient quality. On the other hand, differentiable simulation largely sidesteps these issues by backpropagating through a simulator, but the presence of discrete actions or non-smooth dynamics yields biased or uninformative gradients. To address this, we propose Hybrid Policy Optimization (HPO), which backpropagates through the simulator wherever smoothness permits, using a mixed gradient estimator that combines pathwise and SF gradients while maintaining unbiasedness. We also show how problems with action discontinuities can be reformulated in hybrid form, further broadening its applicability. Empirically, HPO substantially outperforms PPO on inventory control and switched linear-quadratic regulator problems, with performance gaps increasing as the continuous action dimension grows. Finally, we characterize the structure of the mixed gradient, showing that its cross term -- which captures how continuous actions influence future discrete decisions -- becomes negligible near a discrete best response, thereby enabling approximate decentralized updates of the continuous and discrete components and reducing variance near optimality. All resources are available at github.com/MatiasAlvo/hybrid-rl.

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Latest papers 1,138