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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Q-Learning for Reachability in MEC-Free MDPs
Oct 1, 2026Lu-Chin Chang, Suguman BansalMarkov Decision Processes Reinforcement Learning
Georgia Institute of Technology
Reinforcement Learning to Accelerate Primal-Dual Hybrid Gradient for Linear Programming
Oct 1, 2026Jinhwan Sul, Alex Oshin, Evangelos A. TheodorouPrimal-Dual Optimization Reinforcement Learning
Georgia Institute of Technology, USA
Decision Titan: Test-Time Training for Long-Term Memory in Offline Reinforcement Learning
Oct 1, 2026Jude Waide, Robert LieckReinforcement Learning Transformer-Based RL
Department of Computer Science, University of Durham, Durham, United Kingdom.
Rethinking Probability-Based Reinforcement Learning From Posterior Concentration
Oct 1, 2026Shiu-Hong Kao, Yubo Zhao, Zhenyu Tian +3Reinforcement Learning RL for Language Model Reasoning
National University of Singapore · The Hong Kong University of Science and Technology · University of Science and Technology of China
Optimal Transport Meets Reinforcement Learning: A Survey
Oct 1, 2026Yujie Zhu, Charles A. Hepburn, Matthew Thorpe +1Reinforcement Learning Optimal Transport
University of Warwick
MMVistaReason: Toward Open-Data and Post-Training Recipes for Multimodal Reasoning
Oct 1, 2026Juekai Lin, Honglin Lin, Yuqian Yuan +7Reinforcement Learning Multimodal Reasoning
Zhejiang University · Shanghai Artificial Intelligence Laboratory, OpenDataLab · Shanghai Jiao Tong University +1
PPO-HRAP: Proximal Policy Optimization with a Hybrid Regime-Aware Policy for Risk-Controlled Trading
Oct 1, 2026Duong Hien Chi Kien, Thanh Trung HuynhReinforcement Learning Risk-Sensitive RL
College of Engineering and Computer Science, VinUniversity Ha Noi, Viet Nam
MASkillBlender: Decentralized Whole-Body Coordination for Multi-Humanoid Loco-Manipulation via Skill Blending
Oct 1, 2026Yifan Hu, Luhang Hong, Mingkang Long +5Humanoid Loco-Manipulation Reinforcement Learning
Nanyang Technological University · Southeast University · Purple Mountain Laboratories
Probe with Participation Trophies: Random-Reward RL as a Probe of LLM Capability
Oct 1, 2026Yu Mao, Lei Yu, Zining Zhu +6RL for Language Models Reinforcement Learning
University of Toronto · Stevens Institute of Technology · University of California, Santa Cruz +3
RISED: RubrIcs for agentic multi-environment Selection and sElf-Distillation
Oct 1, 2026Jingtan Wang, Sirajul Salekin, Young mok Jung +3Reinforcement Learning Rubric-Based RL
National University of Singapore · Work done during an internship at Apple · Apple
Learning Multiple Timescales for Goal-Conditioned Reinforcement Learning
Oct 1, 2026Pedro Robles Dutenhefner, Dikshant Shehmar, Wagner Meira +1Reinforcement Learning Offline RL
Universidade Federal de Minas Gerais · University of Alberta · Alberta Machine Intelligence Institute (Amii) +1
Crossing the Cyber Divide: Sim-to-Sim and Sim-to-Real Transfer for RL Agents
Sep 30, 2026Sabrina Saika, Yinuo Du, Aritran PiplaiReinforcement Learning Sim-to-Real Transfer
The University of Texas at El Paso, El Paso, TX, USA
Scalable Multi-Task Inverse Reinforcement Learning
Sep 30, 2026Allen Tran, Jia Wan, Nathan Kallus +1Reinforcement Learning Multi-Task RL
Netflix · Massachusetts Institute of Technology · Cornell Tech
ALER: Adaptive Learnable Experience Rewriting for Reinforcement Learning
Sep 30, 2026Oleg Shchendrigin, Egor Cherepanov, Aleksandr I. Panov +1RL Benchmarks Reinforcement Learning
Innopolis University, Innopolis, Russia · MIRIAI, Moscow, Russia · Cognitive AI Systems Lab, Moscow, Russia
Make Sparse Rewards Count: Density-Aware Reward Aggregation for Multi-Reward RL
Sep 30, 2026Tong Zheng, Skylar Zhai, Zhan Cheng +7Multi-Objective Reinforcement Learning Reinforcement Learning
University of Chinese Academy of Sciences · University of Minnesota Twin Cities · University of Wisconsin–Madison +5
PhantomEnvironments: Training LLM Agents in Fictional Worlds
Sep 30, 2026Anmol Kabra, Swathi Saravana Selvam, Albert Gong +5Multi-Hop QA Reinforcement Learning
Cornell University · Stanford University
MemLife: Curating and Reasoning over Long-Term Egocentric Video Memories
Sep 30, 2026Guangzhi Xiong, Xinyuan Zhang, Xiao Yang +14Multimodal Memory Reinforcement Learning
Meta Reality Labs · University of Virginia · Work done at Meta +1
Unlearnable, or Unmeasured? On the Reliability of Difficulty Labels in RLVR
Sep 30, 2026Chandak Chakma, Syed Nazmus Sakib, Nafiul Haque +1Reinforcement Learning RL for Language Model Reasoning
Department of Robotics and Mechatronics Engineering, University of Dhaka
Completion-Aware Cross-Fidelity Offline-to-Online Reinforcement Learning for Multi-Line Bus Holding
Sep 30, 2026Yifan Zhang, Qifan Zhang, Liang ZhengRL Control Reinforcement Learning
Central South University, Changsha, Hunan, China
T-Router: Learning Thalamic Routing for Reasoning with Parameter-Efficient Reinforcement Learning
Sep 30, 2026Liuxian Ma, Jiale Dai, Jiaqi Li +1Reinforcement Learning RL for Language Model Reasoning
College of Artificial Intelligence, Tsinghua University · State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University · Beijing Institute for General Artificial Intelligence
Loop-Free Inverse Reinforcement Learning via Sequential Value Recovery with Q-Score Matching
Sep 30, 2026Yang chen, Yitan Zhang, Michael Witbrock +1Reinforcement Learning
Shanghai Artificial Intelligence Laboratory · University of Auckland
DAMPER: Return-Prioritized Gradient Control for Smooth Policies
Sep 30, 2026Seokmin Ko, Taewon Goo, Kihyuk HongRL Control Reinforcement Learning
KAIST
Revisiting scaling laws for reward optimization
Sep 29, 2026Ali Aouad, Aymane El Gadarri, Vivek F. FariasReward Modeling Reinforcement Learning
MIT
MOBA-VL: Event-Localized Multi-Turn Reinforcement Learning for Real-Time MOBA Commentary
Sep 29, 2026Shengyun Zhong, Xinkang Zhao, Ziyuan Chu +1Reinforcement Learning Video-Language Models
Zhejiang University, China · Northeastern University, USA
Jaxolotl: A Unified High-Performance Benchmark Suite for LTL-Based Multi-Task RL
Sep 29, 2026Mathias Jackermeier, Jacques Cloete, Alessandro AbateRL Benchmarks Reinforcement Learning
Department of Computer Science, University of Oxford · Oxford Robotics Institute, University of Oxford
Unlocking the Critic: Reward-Free Policy Optimization for LLM Post-Training
Sep 29, 2026Hongyang Li, Xiao Li, Caesar Wu +3Reinforcement Learning Value Function Estimation
University of Luxembourg · Seafill Open-Source Community · Université Paris-Saclay
Cool the Sampler, Not the Learner: Sampling Temperature Moves the Staleness Cliff of Importance-Corrected GRPO
Sep 29, 2026Taiheng PanRL for Language Models Reinforcement Learning
The University of Melbourne
SCA: Spatial Credit Assignment for Reinforcement Learning of GUI Agents
Sep 29, 2026Shengtian Yang, Ziyu Xiong, Kaibing Yang +6Reinforcement Learning RL for GUI Agents
Southeast University, Nanjing, China · Kuaishou Technology, Beijing, China · Unaffiliated
State Trace Rationale As Auxiliary Task in Reinforcement Learning
Sep 29, 2026Muhammad U. Nasir, Alex Vogt, Steven D. James +1Reinforcement Learning Representation Learning for RL
University of the Witwatersrand Johannesburg, South Africa · New York University New York, USA
Group-Marginalized Self-Rewarding RL Drives Zero-Label Self-Evolving
Sep 29, 2026Yiming Wang, Yikang Liu, Qingyuan Tian +4RL for Language Models Reinforcement Learning
School of Computer Science, Shanghai Jiao Tong University · Tencent

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Q-Learning for Reachability in MEC-Free MDPs

Lu-Chin Chang, Suguman Bansal

Georgia Institute of Technology

Markov Decision Processes Reinforcement Learning Q-Learning Model-Free RL Policy Learning

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Reinforcement learning (RL) for reachability specifications is fundamental to sequential decision-making. Prior work establishes asymptotic convergence to optimal policies, but only through model-based methods that must explicitly estimate the transition probabilities of the underlying Markov Decision Process (MDP). We present Quasar, the first model-free algorithm with asymptotic guarantees for reachability on the fragment of MDPs free of non-terminal maximal end components (MECs), a building block to which every MDP reduces by the standard MEC quotient. Our algorithm follows the classical Q-learning approach, using temporal-difference updates to converge to an optimal policy without ever learning the transition probabilities. The resulting learner reduces the memory footprint from the O(|S|^2|A|) that model-based methods require to O(|S||A|). On the standardized Quantitative Verification Benchmark Set, our algorithm converges to the optimal policy with orders of magnitude fewer samples than the previous model-based state-of-the-art. Together these results are a concrete step toward the practical deployment of reachability learning and, with it, of specification-guided RL.

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Latest papers 1,138