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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Rethinking the Suitability of Reinforcement Learning Algorithms Under Practical Transfer Constraints
Jul 19, 2026Hany Hamed, Abhishek Naik, Colin Bellinger +1Domain Randomization Reinforcement Learning
University of Alberta · 3Alberta Machine Intelligence Institute (Amii) · 7Generative Bionics +4
When to Plan: Learning to Select Between Reactive Control and Deliberative Planning
Jul 17, 2026Adam Labiosa, Josiah P. HannaRobot Policy Learning Robotic RL
Department of Computer Sciences, University of Wisconsin–Madison, USA
Branching Policy Optimization: Sandbox-Native Language Agent Reinforcement Learning
Jul 15, 2026Bowei He, Yankai Chen, Xiaokun Zhang +1Reinforcement Learning Policy Gradient Methods
Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE · McGill Univeristy, Montreal, Canada · City University of Hong Kong, Hong Kong SAR.
Environment Parameter Gradient Theorem for Policy-Environment Co-Design in Reinforcement Learning
Jul 14, 2026Amber SrivastavaPolicy Learning
Faculty of Mechanical Engineering, Indian Institute of Technology Delhi, India
Learning to Navigate with Minimal Parameters: Decomposing Visual Navigation Through Closed-Form Geometric Interfaces
Jul 13, 2026Edward Beng Wai Tan, Siew-Kei LamVisual Navigation Robot Policy Learning
College of Computing and Data Science, Nanyang Technological University, Singapore.
Actor-Critic Learning for Extended Mean Field Control with Deterministic Policies
Jul 13, 2026Ziheng Cheng, Xin Guo, Huyên Pham +1Policy Gradient Continuous-Time RL
University of California, Berkeley · Ecole Polytechnique, CMAP · Imperial College London
SafeExplorer: An Unbiased Policy Gradient for Reinforcement Learning with Recovery Interventions
Jul 9, 2026Elham Daneshmand, Majid Khadiv, Glen Berseth +1Robot Safety Proximal Policy Optimization
McGill University · Mila · Technical University of Munich +1
Prompt-Driven Exploration
Jul 9, 2026Sunshine Jiang, John Marangola, David Zhang +6LLM-Guided RL RL Exploration
Massachusetts Institute of Technology · Improbable AI Lab · MIT-IBM Computing Research Lab
Latent Memory Palace: Reasoning for Control as Autoregressive Variational Inference
Jul 9, 2026Chuning Zhu, Eva Xu, Jose Barreiros +3RL Control Latent Action Learning
University of Washington · Toyota Research Institute
Expressivity and Statistical Trade-offs in Diffusion Policy Learning
Jul 8, 2026Viet Vu, Renyuan Xu, Jiacheng Zhang +1Diffusion Policy RL for Diffusion Models
Stanford University · Chinese University of Hong Kong · Imperial College London
NFTR: From Provable Mode-Averaging to Geodesic Subgoal Selection in Offline Goal-Conditioned RL
Jul 8, 2026Erdemt Bao, Xing Lei, Jun Chen +2Normalizing Flows Hierarchical RL
Huazhong University of Science and Technology · Xi’an Jiaotong University · University of Electronic Science and Technology of China
Optimal Transport Q-Learning for Flow Policy Steering and Acceleration
Jul 7, 2026Andreas Sochopoulos, Esmeralda S. Whitammer, Nikolaos Tsagkas +3Robot Policy Adaptation Q-Learning
University of Edinburgh · 2Honda Research Institute Europe
Weak-to-Strong Generalization via Direct On-Policy Distillation
Jul 6, 2026Shiyuan Feng, Huan-ang Gao, Haohan Chi +7RL for Language Model Reasoning Weak-to-Strong Generalization
1SIA-Lab of Tsinghua AIR and ByteDance Seed · Institute for AI Industry Research (AIR), Tsinghua University · 4Peking University +1
TREK: Distill to Explore, Reinforce to Refine
Jul 6, 2026Yuanda Xu, Zhengze Zhou, Kayhan Behdin +10RL for Language Model Reasoning LLM-Guided RL
LinkedIn Corporation · Harvard University · Georgia Institute of Technology
Multi-Turn On-Policy Distillation with Prefix Replay
Jul 6, 2026Baohao Liao, Hanze Dong, Christof Monz +3LLM Agent Training On-Policy Distillation
1Microsoft Research · University of Amsterdam
Trust Region Policy Distillation
Jul 6, 2026Zhengpeng Xie, Li Lyna Zhang, Zeke Xie +1On-Policy Distillation Policy Learning
A Hierarchy of Policy Learning Problems
Jul 3, 2026Hamsa Bastani, Osbert Bastani, Shihan ChenSample Complexity Offline RL
Wharton School, University of Pennsylvania · University of Pennsylvania · Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania
ACPO: Adaptive Credit Policy Optimization via Fine-Grained Surrogate Entropy
Jul 3, 2026Zijun Xie, Yuyang You, Yongzhi Li +6Token-Level Credit Assignment RL for Language Model Reasoning
Peking University · Baidu Inc. · Kuaishou Inc.
ASK in the Dark: Uncertainty-Gated LLM Assistance under Partial Observability
Jul 2, 2026Juarez Monteiro, Nathan Gavenski, Guilherme Lima +3Partially Observable RL LLM Prompting
Kunumi Institute, Belo Horizonte, MG, Brazil · King’s College London, London, United Kingdom
Learning Agile Intruder Interception using Differentiable Quadrotor Dynamics
Jul 2, 2026Michael Anoruo, Xiaoyu Tian, Abhishek Rathod +5RL Control Robot Policy Learning
Carnegie Mellon University · Artificial Intelligence Integration Center
Mean Field Reinforcement Learning
Jul 1, 2026René Carmona, Mathieu LaurièreReinforcement Learning Q-Learning
Department of Operations Research and Financial Engineering & Program in Applied and Computational Mathematics, Princeton NJ 08544, USA · Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning; NYU-ECNU Institute of Mathematical Sciences at NYU Shanghai; NYU Shanghai, 567 West Yangsi Road, Shanghai, 200126, People’s Republic of China
Don't Let Gains FADE: Breaking Down Policy Gradient Weights in RL
Jul 1, 2026Juliette Decugis, Sean O'Brien, Francis Bach +2Policy Gradient RL for Language Model Reasoning
FAIR at Meta · Work done at Meta, now working at the University of California, San Diego · Inria, Ecole Normale Supérieure
Procedural Memory Distillation: Online Reflection for Self-Improving Language Models
Jul 1, 2026Ye Liu, Srijan Bansal, Bo Pang +6Language Model Distillation Reinforcement Learning with Verifiable Rewards
Salesforce AI Research
Learning Gait-Aware Quadruped Locomotion with Temporal Logic Specifications
Jul 1, 2026Merve Atasever, Cagan Bakirci, Alfredo Reina Corona +2Reinforcement Learning Robot Skill Learning
University of Southern California, Los Angeles, California, USA
Freeform Preference Learning for Robotic Manipulation
Jun 30, 2026Marcel Torne, Anubha Mahajan, Abhijnya Bhat +1Long-Horizon Robotic Manipulation Robot Skill Learning
Stanford University
TRIAGE: Role-Typed Credit Assignment for Agentic Reinforcement Learning
Jun 30, 2026Yuanda Xu, Zhengze Zhou, Hejian Sang +6Agentic RL Step-Level Credit Assignment in RL
1LinkedIn Corporation · 2Harvard University · 3Johns Hopkins University +1
HyPOLE: Hyperproperty-Guided Multi-Agent Reinforcement Learning under Partial Observation
Jun 29, 2026Arshia Rafieioskouei, Tzu-Han Hsu, Matthew Lucas +1Reinforcement Learning Partially Observable RL
Department of Computer Science and Engineering, Michigan State University, MI, USA.
Predictable GRPO: A Closed-Form Model of Training Dynamics
Jun 29, 2026Rajat Ghosh, Datta Nimmaturi, Aryan Singhal +4Group Relative Policy Optimization Policy Optimization
Nutanix San Jose, USA · Unsloth Hyderabad, India · Nutanix2026 San Jose, USA +1
Pessimism's Paradox: Conservative Offline Training Amplifies Reward Hacking During Online Adaptation in Reasoning Models
Jun 29, 2026Subramanyam Sahoo, Aman Chadha, Vinija Jain +1Reward Modeling Reward Hacking
1Horizon Research · 2Apple · 3Meta +1
ACPO: Agent-Chained Policy Optimization for Multi-Agent Reinforcement Learning
Jun 29, 2026Daiki E. Matsunaga, Junho Na, Tri Wahyu Guntara +4Multi-Agent System Optimization Multi-Agent Coordination
1KAIST · 2KRAFTON · University of Toronto +2

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Rethinking the Suitability of Reinforcement Learning Algorithms Under Practical Transfer Constraints

Hany Hamed, Abhishek Naik, Colin Bellinger, A. Rupam Mahmood

University of Alberta · 3Alberta Machine Intelligence Institute (Amii) · 7Generative Bionics+4

Domain Randomization Reinforcement Learning Robust RL Policy Learning Sample-Efficient RL

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Transfer-oriented reinforcement learning requires evaluating algorithms along dimensions that go beyond standard sample efficiency. We focus on two dimensions: practical efficiency, which asks whether conclusions about algorithm suitability change under wall-clock rather than interaction-based budgets, and robustness under dynamics mismatch, which asks how different learning paradigms respond to variability in the training distribution induced by domain randomization. We provide two insights to reinforcement-learning practitioners. First, comparing the sample efficiency of different algorithms is often an insufficient criterion in transfer-oriented settings. The wall-clock time required to train a decent policy is an important consideration for practitioners, and we find that the sample-inefficient PPO algorithm can produce a performant policy faster than relatively more sample-efficient algorithms such as SAC and TD-MPC2, validating the common understanding of massively parallel training paradigms. Second, domain randomization can help different kinds of algorithms learn robust policies. In particular, although PPO, SAC, and TD-MPC2 represent different RL paradigms - on-policy, off-policy, and model-based learning and planning, respectively - we find that domain randomization affects all three algorithms in a similar way. To the best of our knowledge, this is the first controlled comparison of the effect of domain-randomization coverage on PPO, SAC, and TD-MPC2 under the same transfer protocol. Taken together, these two insights highlight the importance of evaluating RL algorithms not only by sample efficiency, but also by practical considerations such as training time and the algorithms' ability to produce usable policies.

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