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Sample Efficient Offline RL via T-Symmetry Enforced Latent State-Stitching

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
sample efficiencyrepresentation learningfundamental symmetry for dynamic modeling
Abstract:

Offline reinforcement learning (RL) has achieved notable progress in recent years. However, most existing offline RL methods require a large amount of training data to achieve reasonable performance and offer limited out-of-distribution (OOD) generalization capability due to conservative data-related regularizations. This seriously hinders the usability of offline RL in solving many real-world applications, where the available data are often limited. In this study, we introduce TELS, a highly sample-efficient offline RL algorithm that enables state-stitching in a compact latent space regulated by the fundamental time-reversal symmetry (T-symmetry) of dynamical systems. Specifically, we introduce a T-symmetry enforced inverse dynamics model (TS-IDM) to derive well-regulated latent state representations that greatly facilitate OOD generalization. A guide-policy can then be learned entirely in the latent space to optimize for the reward-maximizing next state, bypassing the conservative action-level behavioral regularization adopted in most offline RL methods. Finally, the optimized action can be extracted using the learned TS-IDM, together with the optimized latent next state from the guide-policy. We conducted comprehensive experiments on both the D4RL benchmark tasks and a real-world industrial control test environment, TELS achieves superior sample efficiency and OOD generalization performance, significantly outperforming existing offline RL methods in a wide range of challenging small-sample tasks.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper introduces TELS, an offline RL algorithm leveraging time-reversal symmetry (T-symmetry) to learn compact latent representations that enable state-stitching under severe data scarcity. It resides in the Representation Learning and State Abstraction leaf, which contains only three papers total, indicating a relatively sparse research direction within the broader Sample Efficiency Enhancement Techniques branch. This leaf focuses on exploiting structural properties—symmetries, factorizations, or learned abstractions—to improve generalization from limited offline datasets, distinguishing it from value-based pessimism or behavior cloning approaches that dominate neighboring branches.

The taxonomy reveals that TELS sits adjacent to several related but distinct methodologies. The sibling leaf Data Selection and Prioritization addresses sample efficiency through intelligent filtering rather than representation learning, while Hierarchical and Compositional Learning decomposes tasks into reusable primitives. Nearby branches include Conservative Value Estimation, which handles distributional shift via pessimistic Q-functions, and Model-Based Offline RL, which learns environment dynamics for planning. TELS diverges by embedding temporal symmetry constraints directly into latent state representations, bypassing action-level behavioral regularization common in Policy Regularization and Behavior Constraints methods. This positions the work at the intersection of representation learning and structural exploitation, a less crowded area compared to the heavily populated conservative value estimation cluster.

Among the three contributions analyzed, the literature search examined twenty candidates total. The TS-IDM component (Contribution 1) faced ten candidates with zero refutations, suggesting novelty in applying T-symmetry to inverse dynamics modeling for offline RL. Latent space policy optimization (Contribution 2) was not examined against prior work in this analysis. The overall TELS framework (Contribution 3) encountered ten candidates, with one appearing to provide overlapping prior work, indicating some conceptual precedent exists within the limited search scope. These statistics reflect a targeted semantic search, not an exhaustive survey, so the apparent novelty of TS-IDM and partial overlap for TELS should be interpreted cautiously given the modest candidate pool.

Based on the limited search of twenty semantically similar papers, TELS appears to occupy a relatively underexplored niche within representation learning for offline RL, particularly in leveraging temporal symmetries. The sparse population of its taxonomy leaf and low refutation rates for individual contributions suggest meaningful differentiation from existing work, though the single refutation for the overall framework indicates some conceptual overlap. A broader literature review would be needed to confirm whether T-symmetry enforcement represents a genuinely novel angle or builds incrementally on prior symmetry-based methods.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
20
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: Sample efficient offline reinforcement learning with limited data. The field addresses how to extract strong policies from small, fixed datasets without further environment interaction. The taxonomy reveals several major branches: Algorithmic Approaches to Offline RL encompass foundational methods like conservative Q-learning and behavior regularization (e.g., Implicit Q-Learning[1], Behavior Regularized[9]); Sample Efficiency Enhancement Techniques focus on representation learning, state abstraction, and data augmentation to maximize information from scarce samples; Offline-to-Online Transition Methods bridge the gap by initializing online fine-tuning with offline pretraining (e.g., Offline-to-Online[28]); and Theoretical Foundations examine sample complexity bounds and realizability conditions. Domain-Specific Applications demonstrate these ideas in settings ranging from wireless networks (Wireless Network Optimization[3]) to bioprocess control (Bioprocess Optimization[10]), while Active Data Collection and Exploration consider how to strategically gather new data when minimal interaction is permitted. Within Sample Efficiency Enhancement Techniques, representation learning and state abstraction form a particularly active cluster. Works in this area seek compact, generalizable features that enable better generalization from limited trajectories. T-Symmetry State-Stitching[0] sits squarely in this cluster, emphasizing temporal symmetry properties to stitch together state representations and improve sample reuse. Nearby, Approximate Symmetries[26] explores similar structural invariances, while Factored Action Spaces[17] decomposes high-dimensional action representations to reduce sample requirements. These methods contrast with more direct algorithmic interventions like pessimistic value estimation (Pessimistic Q-Learning[11]) or model-based planning (Planning Learned Model[4]), which address sample scarcity through conservative extrapolation or learned dynamics rather than representational efficiency. The interplay between learning better abstractions and designing cautious policy updates remains a central open question, with T-Symmetry State-Stitching[0] contributing a novel angle by leveraging temporal structure to enhance state-level generalization.

Claimed Contributions

T-symmetry Enforced Inverse Dynamics Model (TS-IDM)

The authors propose TS-IDM, a novel model that learns compact latent state and action representations by enforcing time-reversal symmetry and ODE properties. This model comprises state encoders/decoders, a latent inverse dynamics module, and paired latent ODE forward/reverse dynamics predictors, enabling strong out-of-distribution generalization.

10 retrieved papers
Latent space policy optimization with T-symmetry regularization

The authors develop a policy optimization procedure that operates entirely in the learned latent state space, using T-symmetry consistency as a regularizer. This approach enables state-stitching without conservative action-level constraints, allowing exploitation of out-of-distribution actions while maintaining logical generalization.

0 retrieved papers
TELS framework for sample-efficient offline RL

The authors present TELS, an integrated offline reinforcement learning framework that combines TS-IDM with latent space policy optimization. The framework achieves superior sample efficiency by learning policies in a T-symmetry regulated latent space and extracting optimized actions through the learned inverse dynamics model.

10 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

T-symmetry Enforced Inverse Dynamics Model (TS-IDM)

The authors propose TS-IDM, a novel model that learns compact latent state and action representations by enforcing time-reversal symmetry and ODE properties. This model comprises state encoders/decoders, a latent inverse dynamics module, and paired latent ODE forward/reverse dynamics predictors, enabling strong out-of-distribution generalization.

Contribution

Latent space policy optimization with T-symmetry regularization

The authors develop a policy optimization procedure that operates entirely in the learned latent state space, using T-symmetry consistency as a regularizer. This approach enables state-stitching without conservative action-level constraints, allowing exploitation of out-of-distribution actions while maintaining logical generalization.

Contribution

TELS framework for sample-efficient offline RL

The authors present TELS, an integrated offline reinforcement learning framework that combines TS-IDM with latent space policy optimization. The framework achieves superior sample efficiency by learning policies in a T-symmetry regulated latent space and extracting optimized actions through the learned inverse dynamics model.

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