Policy Learning

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79 papers in the last four weeks, up 316% on the four weeks before. 0.8% of all new papers.

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Dynamical Priors as a Training Objective in Reinforcement Learning
Apr 23, 2026Sukesh SubaharanReinforcement Learning Policy Gradient Methods
Coimbatore Medical College Coimbatore, TN India
Learning from the Near Future: Temporal Self-Distillation for RLVR
Apr 22, 2026Chuanyu Qin, Chenxu Yang, Qingyi Si +6Reinforcement Learning with Verifiable Rewards Self-Distillation
Institute of Information Engineering, CAS · School of Cyber Security, UCAS
Occupancy Reward Shaping: Improving Credit Assignment for Offline Goal-Conditioned Reinforcement Learning
Apr 22, 2026Aravind Venugopal, Jiayu Chen, Xudong Wu +3Reward Shaping Temporal Credit Assignment
Carnegie Mellon University · The University of Hong Kong · INFIFORCE Intelligent Technology +1
Ask Only When Needed: Proactive Retrieval from Memory and Skills for Experience-Driven Lifelong Agents
Apr 22, 2026Yuxuan Cai, Wei Li, Jie Zhou +4Lifelong Learning Agents LLM Agent Skill Learning
School of Computer Science and Technology, East China Normal University, Shanghai · Shanghai AI Laboratory
Lever: Inference-Time Policy Reuse under Support Constraints
Apr 22, 2026Ihor Vitenko, Noha Ibrahim, Sihem Amer-YahiaOffline RL Policy Learning
Anhalt University of Applied Sciences Germany · Université Grenoble Alpes Grenoble, France · CNRS, Univ. Grenoble Alpes Grenoble, France
EVPO: Explained Variance Policy Optimization for Adaptive Critic Utilization in LLM Post-Training
Apr 21, 2026Chengjun Pan, Shichun Liu, Jiahang Lin +10RL for Language Models Policy Gradient Methods
Peking University · Fudan University · Shanghai AI Lab +1
Intentional Updates for Streaming Reinforcement Learning
Apr 21, 2026Arsalan Sharifnassab, Mohamed Elsayed, Kris De Asis +2Reinforcement Learning Temporal-Difference Learning
Openmind Research Institute · Department of Computing Science, University of Alberta.
Accelerating trajectory optimization with Sobolev-trained diffusion policies
Apr 21, 2026Théotime Le Hellard, Franki Nguimatsia Tiofack, Quentin Le Lidec +1Trajectory Optimization Trajectory Generation
Inria - Département d’Informatique de l’École normale supérieure, PSL Research University, France · Courant Institute, New York University, USA
Bounded Ratio Reinforcement Learning
Apr 20, 2026Yunke Ao, Le Chen, Bruce D. Lee +5Reinforcement Learning Group Relative Policy Optimization
ETH Zurich · MPI for Intelligent Systems · University of Alberta +2
Reducing Credit Assignment Variance via Counterfactual Reasoning Paths
Apr 20, 2026Fei Ding, Yongkang Zhang, Youwei Wang +1RL for Language Model Reasoning Credit Assignment in RL
Alibaba Group · Tsinghua University
Does "Do Differentiable Simulators Give Better Policy Gradients?'' Give Better Policy Gradients?
Apr 20, 2026Ku Onoda, Paavo Parmas, Manato Yaguchi +1Policy Gradient Robotic RL
The University of Tokyo, Tokyo, Japan
Efficient Federated RLHF via Zeroth-Order Policy Optimization
Apr 20, 2026Deyi Wang, Qining Zhang, Lei YingFederated RL Communication-Efficient Distributed Training
University of Michigan, Ann Arbor
Poly-EPO: Training Exploratory Reasoning Models
Apr 19, 2026Ifdita Hasan Orney, Jubayer Ibn Hamid, Shreya S Ramanujam +5Reinforcement Learning RL for Language Model Reasoning
Stanford University
Freshness-Aware Prioritized Experience Replay for LLM/VLM Reinforcement Learning
Apr 18, 2026Weiyu Ma, Yongcheng Zeng, Yan Song +4Reinforcement Learning RL Post-Training
King Abdullah University of Science and Technology (KAUST) · Chinese Academy of Sciences, Institute of Automation (CASIA) · AI Centre, Department of Computer Science, University College London +1
Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model
Apr 17, 2026Jean Tarbouriech, Matteo Pirotta, Michal Valko +1Markov Decision Processes Policy Learning
Facebook AI Research Paris & Inria Lille · Facebook AI Research Paris · DeepMind Paris
Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning
Apr 16, 2026Lute Lillo, Nick CheneyNon-Stationary RL Reinforcement Learning
Department of Computer Science University of Vermont USA
Beyond Importance Sampling: Rejection-Gated Policy Optimization
Apr 16, 2026Ziwu Sun, Zhen Gao, Jiyong Zhang +1Importance Sampling Policy Optimization
Mean Flow Policy Optimization
Apr 16, 2026Xiaoyi Dong, Xi Sheryl Zhang, Jian ChengMeanFlow Mean Flow
C2DL, Institute of Automation, Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences · AiRiA +1
Policy Improvement Reinforcement Learning
Apr 1, 2026Huaiyang Wang, Xiaojie Li, Xiaohan Wang +10RL for Language Models RL for Language Model Reasoning
Beihang University · Meituan · Peking University
End-to-End Efficient RL for Linear Bellman Complete MDPs with Deterministic Transitions
Mar 24, 2026Zakaria Mhammedi, Alexander Rakhlin, Nneka OkoloReinforcement Learning Linear Markov Decision Processes
Google Research, NYC · MIT
DeepStock: Reinforcement Learning with Policy Regularizations for Inventory Management
Mar 20, 2026Yaqi Xie, Xinru Hao, Jiaxi Liu +4Inventory Control Reinforcement Learning
Booth School of Business, University of Chicago, Chicago, USA. · Taobao & Tmall Group, Hangzhou, China. · School of Economics, Sichuan University, Chengdu, China. +2
From Prior to Pro: Efficient Skill Mastery via Distribution Contractive RL Finetuning
Mar 10, 2026Zhanyi Sun, Shuran SongRL for Robotics Robot Policy Learning
1Stanford University.
Reinforcing the World's Edge: A Continual Learning Problem in the Multi-Agent-World Boundary
Mar 6, 2026Dane MalenfantNon-Stationary RL Decentralized MARL
School of Computer Science McGill University Mila - The Québec AI Institute
Temporal Consistency Improves Generalization in Contextual Offline Meta Reinforcement Learning
Mar 3, 2026Mohammadreza Nakheai, Aidan Scannell, Kevin Luck +1Representation Learning Offline RL
Aalto University · Toyota Research Institute · Vrije Universiteit Amsterdam
Multi-agent imitation learning with function approximation: Linear Markov games and beyond
Feb 26, 2026Luca Viano, Till Freihaut, Emanuele Nevali +3Game Theory Imitation Learning
1EPFL · University of Zurich · 3Earth Species Project
ARLArena: A Unified Framework for Stable Agentic Reinforcement Learning
Feb 25, 2026Xiaoxuan Wang, Han Zhang, Haixin Wang +11Agentic RL Reinforcement Learning
Department of Computer Science, University of California, Los Angeles, California, USA · University of Wisconsin–Madison, Madison, Wisconsin, USA
Gradient Regularization Mitigates Reward Hacking in Reinforcement Learning from Human Feedback and Verifiable Rewards
Feb 20, 2026Johannes Ackermann, Michael Noukhovitch, Takashi Ishida +1Reward Hacking Reinforcement Learning with Verifiable Rewards
The University of Tokyo · Mila · RIKEN AIP
TSR: Trajectory-Search Rollouts for Multi-Turn RL of LLM Agents
Feb 12, 2026Aladin Djuhera, Swanand Kadhe, Farhan Ahmed +3Tree Search LLM Agent Training
Technical University Munich · IBM Research
On the Role of Computation in Reinforcement Learning
Feb 5, 2026Raj Ghugare, Michał Bortkiewicz, Alicja Ziarko +1Reinforcement Learning Model-Free RL
Department of Computer Science, Princeton University · Warsaw University of Technology · University of Warsaw +2
Constrained Group Relative Policy Optimization
Feb 5, 2026Roger Girgis, Rodrigue de Schaetzen, Luke Rowe +3Group Relative Policy Optimization Constrained RL
Mila – Quebec AI Institute Université de Montréal

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

Dynamical Priors as a Training Objective in Reinforcement Learning

Sukesh Subaharan

Coimbatore Medical College Coimbatore, TN India

Reinforcement Learning Policy Gradient Methods Policy Learning

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Standard reinforcement learning (RL) optimizes policies for reward but imposes few constraints on how decisions evolve over time. As a result, policies may achieve high performance while exhibiting temporally incoherent behavior such as abrupt confidence shifts, oscillations, or degenerate inactivity. We introduce Dynamical Prior Reinforcement Learning (DP-RL), a training framework that augments policy gradient learning with an auxiliary loss derived from external state dynamics that implement evidence accumulation and hysteresis. Without modifying the reward, environment, or policy architecture, this prior shapes the temporal evolution of action probabilities during learning. Across three minimal environments, we show that dynamical priors systematically alter decision trajectories in task-dependent ways, promoting temporally structured behavior that cannot be explained by generic smoothing. These results demonstrate that training objectives alone can control the temporal geometry of decision-making in RL agents.

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