State Representation Learning

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13 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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Factorized Spectral Representations for Reinforcement Learning
Jul 15, 2026Junyi Wu, Dan LiTensor Decomposition Representation Learning for RL
Department of Industrial and Systems Engineering University of Washington Seattle, WA, USA
When Does Reward Teach State? A Hidden-Automaton Instrument and a Group-Language Warning Signal
Jul 11, 2026James E. AllchinReinforcement Learning Partially Observable RL
Independent Researcher
Discovering Latent Response Laws in Forced Physical Systems
Jul 9, 2026Yi Zhu, Su Chen, Xiaojun Li +1Dynamical Systems Latent Dynamics Modeling
State Key Laboratory of Bridge Safety and Resilience, Beijing University of Technology, Beijing, 100124, China
Liquid Latent State Dynamics for Interpretable Turbofan Degradation Modeling
Jul 2, 2026Weizhi Nie, Weijie Wang, Yuting SuMultivariate Time Series Forecasting Liquid Neural Networks
Tianjin University
A Task-State Representation for Long-Horizon Mobile GUI Agents
Jul 1, 2026Yujie Zheng, Zikang Liu, Xin Zhao +1Mobile GUI Automation State Representation Learning
School of Software, Beihang University · Gaoling School of Artificial Intelligence, Renmin University of China
Latent Actions from Factorized Transition Effects under Agent Ambiguity
Jun 29, 2026Heejeong Nam, Chandradithya S Jonnalagadda, Harshit Aggarwal +2Latent Action Learning Latent Dynamics Modeling
Brown University
Predictive Objectives Discard Exogenous Control-Relevant Features: A Controlled Mechanistic Study
Jun 29, 2026Ayan PendharkarRepresentation Learning Joint-Embedding Predictive Architecture
Building artificial intelligence virtual tissue (AIVT) for tissue state representation, feature prediction, and dynamic simulation
Jun 29, 2026Qiqi Lu, Qianjin Feng, Shaoqun Zeng +1State Representation Learning
School of Biomedical Engineering, Southern Medical University, Guangzhou, China. · Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China. · Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China. +1
Textual Belief States for World Models: Identifiable Representation Learning Under Strict Mediation
Jun 26, 2026Xiang Gao, Kaiwen Dong, Yuguang Yao +2Representation Learning Group Relative Policy Optimization
Intuit AI Research
State Representation Matters in Deep Reinforcement Learning: Application to Energy Trading
Jun 25, 2026Jesper Klicks, Sander Vržina, Vincent François-LavetState Representation Learning Power Systems
ASALT: Adaptive State Alignment for Lateral Transfer in Multi-agent Reinforcement Learning
Jun 23, 2026Anurag Akula, Satheesh K. Perepu, Abhishek Sarkar +1Cross-Domain Transfer Learning State Representation Learning
Indian Institute of Technology Madras, India · Ericsson Research, India
Sensorimotor World Models: Perception for Action via Inverse Dynamics
Jun 18, 2026Petr Ivashkov, Randall Balestriero, Bernhard SchölkopfRepresentation Learning World Model-Based Planning
Max Planck Institute for Intelligent Systems, Tübingen, Germany · Department of Computer Science, Brown University, Providence, RI, USA · ELLIS Institute, Tübingen, Germany +1
Structured Representation Learning with Locally Linear Embeddings and Adaptive Feature Fusion
Jun 16, 2026Somjit Nath, Jackson J Cone, Derek Nowrouzezahrai +1Disentangled Representation Learning State Representation Learning
Department of Electrical and Computer Engineering McGill University Mila – Quebec AI Institute · Department of Psychology Department of Biomedical Engineering University of Calgary Canada Research Chair in Computational Behavioural Neuroscience (Tier II) · Department of Electrical and Software Engineering Schulich School of Engineering University of Calgary Mila – Quebec AI Institute Canada CIFAR AI Chair
Performance-Driven Environment Abstraction with Multi-Timescale Learning
Jun 16, 2026Yue Guan, Dipankar Maity, Panagiotis TsiotrasMarkov Decision Processes Reinforcement Learning
School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USA · Department of Electrical and Computer Engineering, University of North Carolina at Charlotte, Charlotte, NC, USA
Two-Layer Linear Auto-Regressive Models Estimate Latent States
Jun 10, 2026Yahya Sattar, Sunmook Choi, Leo Maynard-Zhang +3Dynamical Systems Kalman Filtering
Cornell · U Washington · Imperial College London +1
RePAIR: Predictive Self-Supervised Representation Learning in Chess
Jun 10, 2026Christoph Koller, Johannes Fürnkranz, Timo BertramMasked Autoencoders Joint-Embedding Predictive Architecture
JKU Linz, Austria
GIFT: LLM-Guided State-Reward Interface for Financial Reinforcement Learning
Jun 7, 2026Yanyan Wu, Boyi Zhang, Yanlin Liu +10Reinforcement Learning Reward Shaping
East China University of Science and Technology · University of Science and Technology of China · Southwestern University of Finance and Economics +5
Adaptive state-action abstractions via rate-distortion
Jun 4, 2026Fernando E. RosasState Abstraction State Representation Learning
Department of Informatics, University of Sussex · Department of Brain Science, Imperial College London · Centre for Eudaimonia and Human Flourishing, University of Oxford
Task-Induced Representational Invariances Depend on Learning Objective in Deep RL
Jun 1, 2026Manu Srinath Halvagal, Sebastian Lee, SueYeon ChungMarkov Decision Processes Deep Q-Learning
Department of Physics, Harvard University · 2Kempner Institute, Harvard University · Center for Computational Neuroscience, Flatiron Institute
Richer Representations for Neural Algorithmic Reasoning via Auxiliary Reconstruction
May 30, 2026Jiafu Huang, Chao Peng, Chenyang Xu +7Neural Algorithmic Reasoning State Representation Learning
Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, China · Overseas Education College, Xiamen University, China · Dept. of Electrical and Electronic Engineering, The University of Melbourne, Australia
The Terminal Representation in Reinforcement Learning
May 29, 2026Amir Esterhuysen, Anders JonssonRepresentation Learning for RL Trajectory Representation Learning
Dept. Information and Communication Technologies Universitat Pompeu Fabra Barcelona, Spain
Why Linear Recurrent Memory Works in Partially Observable Reinforcement Learning
May 29, 2026Yike Zhao, Onno Eberhard, Malek Khammassi +2Partially Observable RL Markov Models
EPFL, Lausanne, Switzerland · Max Planck Institute for Intelligent Systems, Tübingen, Germany
STEP: Learning STructured Embeddings for Progressive Time Series
May 29, 2026Lucas Thil, Jesse Read, Rim Kaddah +1Self-Supervised Time Series Representation Learning Interpretable ML
LIX, École Polytechnique IRT SystemX Palaiseau, France · IRT SystemX Palaiseau, France · Safran Tech Chateaufort, France
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
Mitigating State Aliasing in Vision-Language-Action Models via Inverse Dynamics Learning
May 28, 2026Kyujin Lee, Injae Kim, Jihwan Park +3Vision-Language-Action Models State Representation Learning
KAIST · Korea University
Teacher-Student Representational Alignment for Reinforcement Learning-Driven Imitation Learning
May 27, 2026Meraj Mammadov, Pedro Zuidberg Dos Martires, Johannes Andreas StorkImitation Learning Robot Imitation Learning
Department of Computer Science Örebro University
Exploratory Experience Shapes the Geometry of Predictive Representations
May 27, 2026Kseniia Shilova, Abdelrahman Sharafeldin, Advay Balakrishnan +1Computational Cognitive Modeling Predictive Coding
School of Mathematics Georgia Institute of Technology Atlanta, GA 30332 · College of Computing Georgia Institute of Technology Atlanta, GA 30332
Learning in Low-Dimensional Subspaces: Orthogonal Bottlenecks for Reinforcement Learning
May 25, 2026Aleksandar Todorov, Matthia SabatelliRepresentation Learning Reinforcement Learning
University of Groningen, Groningen, The Netherlands
Smaller Abstract State Spaces Enable Cross-Scale Generalization in Reinforcement Learning
May 19, 2026Nasehatul Mustakim, Lucas LehnertPartially Observable RL OOD Generalization
Department of Computer Science University of Saskatchewan Saskatoon, Saskatchewan, Canada
One Model, Two Roles: Emergent Specialization in a Shared Recurrent Transformer
May 18, 2026Jucheng Shen, Barbara Su, Anastasios KyrillidisTransformer Interpretability Recurrent Transformers
Rice University

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Factorized Spectral Representations for Reinforcement Learning

Junyi Wu, Dan Li

Department of Industrial and Systems Engineering University of Washington Seattle, WA, USA

Tensor Decomposition Representation Learning for RL State Representation Learning Sample-Efficient RL

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Learning a compact model of the world from interaction data is central to sample-efficient deep reinforcement learning. Spectral representation methods have become the leading paradigm for representation learning in continuous control by taking a matrix view of the transition kernel, with state-action pairs on one side and next states on the other, and learning a low-rank factorization through self-supervised contrastive objectives. We take this view one step further. The transition kernel is naturally a three-mode tensor over states, actions, and next states, and a CP decomposition gives one feature map per mode. We propose FaStR, which fits this decomposition with a noise contrastive objective, producing separate state, action, and next-state encoders that together form a single spectral representation. The factored form yields a smaller hypothesis class, and the sample size needed for representation learning shrinks by a factor that scales with the smaller of the state and action dimensions. Empirically, FaStR delivers its largest gains on high-dimensional locomotion tasks whose dynamics align with the factored structure, and the learned state encoder transfers intact across actuator shift while only the action encoder is retrained.

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