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ADM-v2: Pursuing Full-Horizon Roll-out in Dynamics Models for Offline Policy Learning and Evaluation

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Model-based Reinforcement LearningOffline Reinforcement Learning
Abstract:

Model-based methods for offline Reinforcement Learning transfer extensive policy exploration and evaluation to data-driven dynamics models, effectively saving real-world samples in the offline setting. We expect the dynamics model to allow the policy to roll out full-horizon episodes, which is crucial for ensuring sufficient exploration and reliable evaluation. However, many previous dynamics models exhibit limited capability in long-horizon prediction. This work follows the paradigm of the Any-step Dynamics Model (ADM) that improves future predictions by reducing bootstrapping prediction to direct prediction. We structurally decouple each recurrent forward of the RNN cell from the backtracked state and propose the second version of ADM (ADM-v2), making the direct prediction more flexible. ADM-v2 not only enhances the accuracy of direct predictions for making full-horizon roll-outs but also supports parallel estimation of the any-step prediction uncertainty to improve efficiency. The results on DOPE validate the reliability of ADM-v2 for policy evaluation. Moreover, via full-horizon roll-out, ADM-v2 for policy optimization enables substantial advancements, whereas other dynamics models degrade due to long-horizon error accumulation. We are the first to achieve SOTA under the full-horizon roll-out setting on both D4RL and NeoRL.

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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 proposes ADM-v2, a dynamics model architecture that decouples recurrent forward passes from backtracked states to enable direct multi-step prediction for long-horizon offline reinforcement learning. It resides in the Multi-Step and Direct Prediction Models leaf, which contains five papers total, including the original ADM-v2 submission. This leaf sits within the broader Dynamics Model Architecture and Prediction Horizon branch, indicating a moderately populated research direction focused on reducing error accumulation through direct rather than bootstrapped forecasting. The taxonomy reveals this is an active but not overcrowded area, with sibling leaves exploring diffusion-based and latent world model alternatives.

The taxonomy tree shows neighboring leaves include Diffusion-Based Dynamics Models (four papers) and Latent and Hierarchical World Models (five papers), both addressing long-horizon prediction through different architectural paradigms. ADM-v2 diverges from diffusion approaches by pursuing deterministic multi-step forecasting rather than generative sampling, and from latent world models by operating directly in state space without learned abstractions. The scope note for the parent branch explicitly excludes policy learning frameworks, clarifying that ADM-v2's contribution centers on dynamics architecture rather than value estimation or hierarchical decomposition, which belong under separate branches.

Among the three identified contributions, the literature search examined 23 candidates total, with 10 papers analyzed for both the structural decoupling architecture and the PARoll algorithm, and 3 for the full-horizon roll-out framework. None of the contributions were clearly refuted by the limited candidate set. The architectural decoupling and parallel estimation mechanisms appear relatively novel within the examined scope, though the search scale (23 papers from semantic matching) means substantial prior work outside this candidate pool cannot be ruled out. The full-horizon roll-out framework, examined against only 3 candidates, shows the least coverage but also no direct overlap.

Based on the limited search scope of 23 semantically matched candidates, ADM-v2 appears to introduce architectural refinements within an established research direction. The taxonomy context suggests the work builds incrementally on multi-step prediction paradigms rather than opening entirely new territory, though the specific decoupling mechanism and parallel uncertainty estimation may offer meaningful technical advances. The analysis does not cover exhaustive citation networks or broader model-based offline RL literature beyond the top-K semantic matches.

Taxonomy

Core-task Taxonomy Papers
45
3
Claimed Contributions
23
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: long-horizon dynamics modeling for offline reinforcement learning. The field addresses how to learn predictive models from fixed datasets and use them to plan or optimize policies over extended time horizons without further environment interaction. The taxonomy organizes research into several main branches: Dynamics Model Architecture and Prediction Horizon explores how models represent and forecast future states, ranging from single-step recurrent approaches to multi-step direct predictors and diffusion-based world models like Diffusion world model[4] and Diffusion World Model[21]; Policy Learning and Value Estimation focuses on integrating learned dynamics with policy optimization and value functions, including methods such as Q-value Regularized Transformer for[5] and OPAL[6]; Hierarchical and Compositional Approaches decompose long-horizon problems into subgoals or temporal abstractions, exemplified by works like Latent plans for task-agnostic[2] and Learning temporally abstractworld models[10]; Transfer Learning and Task Generalization investigates how dynamics models can support generalization across tasks or domains; and Specialized Applications and Extensions covers domain-specific adaptations and safety-constrained settings. A central tension across these branches concerns the trade-off between model expressiveness and compounding error over long rollouts. Multi-step prediction methods such as A Multi-step Loss Function[15] and Multi-timestep models for Model-based[22] attempt to mitigate error accumulation by training on extended sequences, while diffusion-based approaches offer flexible generative modeling at the cost of computational overhead. ADM-v2[0] sits within the Multi-Step and Direct Prediction Models cluster, emphasizing direct long-horizon forecasting to reduce iterative error propagation. Compared to neighbors like Diffusion world model[4], which leverages generative diffusion for state prediction, ADM-v2[0] pursues a more deterministic multi-step architecture, and relative to A Multi-step Loss Function[15], it extends the prediction horizon further while refining training objectives. These contrasting strategies highlight ongoing questions about how best to balance model capacity, training stability, and planning efficiency when offline data limits the ability to correct predictive mistakes through online interaction.

Claimed Contributions

ADM-v2 architecture with structural decoupling

The authors introduce ADM-v2, a new dynamics model architecture that decouples the GRU cell's recurrent forward operations from the backtracked state. This structural modification improves the flexibility and reliability of direct multi-step predictions compared to the original ADM.

10 retrieved papers
Parallel Any-step Roll-out (PARoll) algorithm

The authors develop PARoll, an efficient roll-out algorithm that enables parallel computation of any-step predictions and uncertainty estimation in ADM-v2. This algorithm discards the backtracking mechanism of the original ADM and supports efficient full-horizon roll-outs.

10 retrieved papers
Full-horizon roll-out framework for offline policy learning and evaluation

The authors propose a framework (ADM2PO-fh) that leverages full-horizon roll-outs in ADM-v2 for both offline policy optimization and evaluation. They incorporate any-step uncertainty as a penalty in Q-value estimation and demonstrate state-of-the-art performance on D4RL and NeoRL benchmarks.

3 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

ADM-v2 architecture with structural decoupling

The authors introduce ADM-v2, a new dynamics model architecture that decouples the GRU cell's recurrent forward operations from the backtracked state. This structural modification improves the flexibility and reliability of direct multi-step predictions compared to the original ADM.

Contribution

Parallel Any-step Roll-out (PARoll) algorithm

The authors develop PARoll, an efficient roll-out algorithm that enables parallel computation of any-step predictions and uncertainty estimation in ADM-v2. This algorithm discards the backtracking mechanism of the original ADM and supports efficient full-horizon roll-outs.

Contribution

Full-horizon roll-out framework for offline policy learning and evaluation

The authors propose a framework (ADM2PO-fh) that leverages full-horizon roll-outs in ADM-v2 for both offline policy optimization and evaluation. They incorporate any-step uncertainty as a penalty in Q-value estimation and demonstrate state-of-the-art performance on D4RL and NeoRL benchmarks.

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