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MAGE: Multi-scale Autoregressive Generation for Offline Reinforcement Learning

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 5.5Download Report PDF
Offline Reinforcement Learning; Auto-Regressive; Multi-Scale; Long-horizon
Abstract:

Generative models have gained significant traction in offline reinforcement learning (RL) due to their ability to model complex trajectory distributions. However, existing generation-based approaches still struggle with long-horizon tasks characterized by sparse rewards. Some hierarchical generation methods have been developed to mitigate this issue by decomposing the original problem into shorter-horizon subproblems using one policy and generating detailed actions with another. While effective, these methods often overlook the multi-scale temporal structure inherent in trajectories, resulting in suboptimal performance. To overcome these limitations, we propose MAGE, a Multi-scale Autoregressive GEneration-based offline RL method. MAGE incorporates a condition-guided multi-scale autoencoder to learn hierarchical trajectory representations, along with a multi-scale transformer that autoregressively generates trajectory representations from coarse to fine temporal scales. MAGE effectively captures temporal dependencies of trajectories at multiple resolutions. Additionally, a condition-guided decoder is employed to exert precise control over short-term behaviors. Extensive experiments on five offline RL benchmarks against fifteen baseline algorithms show that MAGE successfully integrates multi-scale trajectory modeling with conditional guidance, generating coherent and controllable trajectories in long-horizon sparse-reward settings.

Disclaimer
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.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes MAGE, a multi-scale autoregressive generation method for offline reinforcement learning targeting long-horizon sparse-reward tasks. According to the taxonomy, MAGE belongs to the 'Autoregressive Multi-Scale Generation' leaf under 'Multi-Scale Trajectory Modeling'. This leaf currently contains only the original paper itself, with no sibling papers identified. The broader 'Multi-Scale Trajectory Modeling' branch contains just two leaves—autoregressive and diffusion-based approaches—suggesting this is a relatively sparse and emerging research direction within the field.

The taxonomy reveals that MAGE sits adjacent to 'Diffusion-Based Multi-Scale Generation', which includes work on hierarchical diffusion models for trajectory synthesis. The broader field is dominated by 'Hierarchical Decomposition Approaches' with multiple subtopics (goal-conditioned, skill-based, symbolic planning) and 'Model-Based and Planning-Centric Methods' covering latent planning, transformers, and value-based orchestration. MAGE's focus on autoregressive multi-scale generation distinguishes it from hierarchical methods that impose explicit high-low level separation and from diffusion approaches that use iterative refinement rather than sequential coarse-to-fine synthesis.

Among 21 candidates examined, the contribution-level analysis reveals mixed novelty signals. The core MAGE framework (9 candidates examined, 0 refutable) and the condition-guided autoencoder (2 candidates, 0 refutable) appear to have no clear prior work within the limited search scope. However, the multi-scale transformer with condition-guided decoder (10 candidates examined, 2 refutable) shows potential overlap with existing methods. These statistics suggest that while the overall approach may be novel, specific architectural components have precedents among the examined candidates.

Based on the limited search scope of 21 semantically related papers, MAGE appears to occupy a sparsely populated niche combining autoregressive generation with multi-scale trajectory modeling. The analysis does not cover exhaustive prior work across all related conferences or workshops, and the refutable pairs identified for one contribution warrant careful examination during full review. The taxonomy context suggests MAGE extends multi-scale modeling ideas into a less-explored autoregressive paradigm.

Taxonomy

Core-task Taxonomy Papers
23
3
Claimed Contributions
21
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: multi-scale trajectory generation for long-horizon sparse-reward offline reinforcement learning. The field addresses the challenge of learning effective policies from fixed datasets when rewards are infrequent and tasks require extended sequences of actions. The taxonomy reveals several complementary research directions: Hierarchical Decomposition Approaches break down complex tasks into manageable subgoals, often leveraging goal-conditioned policies or skill discovery; Multi-Scale Trajectory Modeling focuses on representing and generating trajectories at varying temporal resolutions, enabling both coarse planning and fine-grained control; Model-Based and Planning-Centric Methods emphasize learned dynamics models or search procedures to guide decision-making; Navigation and Vision-Language Tasks apply these ideas to embodied agents operating in spatial or multimodal environments; and Specialized Optimization and Fusion Techniques develop novel training objectives or architectural innovations. Works such as HIQL[8] and Hierarchical Sparse Rewards[1] exemplify hierarchical decomposition, while Guide to Control[4] and IQL-TD-MPC[23] illustrate model-based planning strategies. A central tension across these branches is the trade-off between explicit temporal abstraction and end-to-end learning of multi-scale representations. Hierarchical methods like HIQL[8] and Offline HRL Training[14] impose structured decompositions that can simplify credit assignment, yet may require careful design of subgoal spaces. In contrast, autoregressive and diffusion-based approaches such as Structural Hierarchical Diffusion[6] and Discrete Diffusion Skills[17] learn multi-scale structure more implicitly, potentially offering greater flexibility but demanding robust generative modeling. MAGE[0] sits within the Multi-Scale Trajectory Modeling branch, specifically under Autoregressive Multi-Scale Generation, emphasizing learned temporal hierarchies without rigid decomposition. Compared to DASP[3], which also explores autoregressive generation, MAGE[0] appears to prioritize scalable multi-resolution synthesis, while works like Coarse-To-Fine Imitation[15] blend hierarchical planning with imitation learning. These contrasting strategies highlight ongoing questions about how best to balance structure, flexibility, and sample efficiency in long-horizon offline settings.

Claimed Contributions

MAGE: Multi-scale Autoregressive Generation method for offline RL

The authors introduce MAGE, a novel offline reinforcement learning approach that generates trajectories in a coarse-to-fine manner across multiple temporal scales. This method addresses challenges in long-horizon tasks with sparse rewards by capturing both global trajectory structure and local temporal dynamics through hierarchical autoregressive generation.

9 retrieved papers
Condition-guided multi-scale autoencoder for hierarchical trajectory representations

The method includes a multi-scale autoencoder that encodes trajectories into hierarchical latent representations at different temporal resolutions, from coarse global structure to fine-grained details. This component enables the model to capture multi-scale temporal dependencies in trajectories.

2 retrieved papers
Multi-scale transformer with condition-guided decoder

The authors develop a multi-scale transformer that autoregressively generates trajectory token maps sequentially from coarse to fine scales, with each finer scale conditioned on coarser ones. A condition-guided decoder module is integrated to ensure precise control over generated trajectories based on specified conditions like return-to-go and initial state.

10 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

MAGE: Multi-scale Autoregressive Generation method for offline RL

The authors introduce MAGE, a novel offline reinforcement learning approach that generates trajectories in a coarse-to-fine manner across multiple temporal scales. This method addresses challenges in long-horizon tasks with sparse rewards by capturing both global trajectory structure and local temporal dynamics through hierarchical autoregressive generation.

Contribution

Condition-guided multi-scale autoencoder for hierarchical trajectory representations

The method includes a multi-scale autoencoder that encodes trajectories into hierarchical latent representations at different temporal resolutions, from coarse global structure to fine-grained details. This component enables the model to capture multi-scale temporal dependencies in trajectories.

Contribution

Multi-scale transformer with condition-guided decoder

The authors develop a multi-scale transformer that autoregressively generates trajectory token maps sequentially from coarse to fine scales, with each finer scale conditioned on coarser ones. A condition-guided decoder module is integrated to ensure precise control over generated trajectories based on specified conditions like return-to-go and initial state.

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