OpenNovelty.org

RIG: Synergizing Reasoning and Imagination in End-to-End Generalist Policy

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Multi-modal Embodied AgentUnified Generative ModelAuto-Regressive World Model
Abstract:

Reasoning before action and imagining potential outcomes (i.e., world models) are essential for embodied agents operating in complex open-world environments. Yet, prior work either incorporates only one of these abilities in an end-to-end agent or integrates multiple specialized models into an agent system, limiting the learning efficiency and generalization of the policy. Thus, this paper makes the first attempt to synergize Reasoning and Imagination in an end-to-end Generalist policy, termed RIG. To train RIG in an end-to-end manner, we construct a data pipeline that progressively integrates and enriches the content of imagination and reasoning in the trajectories collected from existing agents. The joint learning of reasoning and next image generation explicitly models the inherent correlation between reasoning, action, and dynamics of environments. It thus exhibits more than 17×17\times sample efficiency improvements and generalization in comparison with previous works. During inference, RIG first reasons about the next action, produces potential action, and then predicts the action outcomes, which offers the agent a chance to review and self-correct based on the imagination before taking real actions. Experimental results show that the synergy of reasoning and imagination not only improves the robustness, generalization, and interoperability of generalist policy but also enables test-time scaling to enhance overall performance.

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 an end-to-end generalist policy that jointly learns reasoning and imagination for embodied agents. It occupies a unique position in the taxonomy: the 'Integrated Reasoning and Imagination for Generalist Policies' leaf contains only this single paper, making it the sole representative of this specific research direction. In contrast, neighboring branches such as 'Robotic Manipulation with Vision-Language Reasoning' contain multiple subtopics with 15+ papers, and 'World Models and Predictive Simulation' includes 4 papers across several leaves. This isolation suggests the paper targets a relatively unexplored integration strategy within the broader field of embodied AI.

The taxonomy reveals substantial activity in related but distinct directions. The 'World Models and Predictive Simulation' branch (4 papers) focuses on learning environment dynamics separately, while 'Multimodal Reasoning and Visual Imagination' (3 papers) emphasizes visual chain-of-thought without embodied action execution. The 'Robotic Manipulation' branch explores affordance reasoning and simulation-based verification (7 papers) but typically employs modular architectures. RIG's approach diverges by unifying reasoning and imagination within a single policy framework, contrasting with the modular pipelines prevalent in navigation (e.g., NavCoT in 'Chain-of-Thought Enhanced Navigation') and manipulation (e.g., CubeRobot in 'Ambiguity Resolution') categories.

Among the 30 candidates examined through semantic search, none clearly refute any of the three core contributions. The first contribution (end-to-end synergy) examined 10 candidates with 0 refutable matches; the second (progressive data collection) and third (test-time lookahead) each examined 10 candidates with identical results. This absence of overlapping prior work within the limited search scope suggests the specific combination of reasoning and imagination in a unified generalist policy has not been extensively documented in the top-30 semantically similar papers. However, this reflects the bounded search strategy rather than an exhaustive field survey.

The analysis indicates the paper occupies a sparse research direction within a field that otherwise exhibits concentrated activity in modular or task-specific approaches. The limited search scope (30 candidates) and absence of sibling papers in the same taxonomy leaf suggest novelty in the integration strategy, though neighboring work on world models and multimodal reasoning provides relevant context. The contribution-level statistics uniformly show no clear refutations, but this should be interpreted cautiously given the non-exhaustive nature of the literature search.

Taxonomy

Core-task Taxonomy Papers
49
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: synergizing reasoning and imagination in embodied agents. The field encompasses a diverse set of approaches that combine symbolic or language-based reasoning with predictive or generative imagination to enable robots and virtual agents to act intelligently in complex environments. At the top level, the taxonomy organizes work into branches such as Vision-Language Navigation and Spatial Reasoning, which focuses on grounding language instructions in spatial contexts; Robotic Manipulation with Vision-Language Reasoning, emphasizing object-level interaction; Multimodal Reasoning and Visual Imagination, where agents generate or interpret visual scenarios; World Models and Predictive Simulation, which build forward models of environment dynamics; and Integrated Reasoning and Imagination for Generalist Policies, targeting unified architectures that blend both capacities. Additional branches address Abstract Reasoning and Commonsense Knowledge, Cognitive Architectures and Theoretical Frameworks (e.g., Planning Active Inference[9], Embodied Cognition Learning[22]), Reinforcement Learning with Imagination (e.g., Imagination Augmented DRL[27]), Memory-Guided Exploration and Verification, and Socially-Aware and Human-Robot Interaction. Together, these branches reflect a spectrum from task-specific navigation and manipulation methods to broader cognitive models and theoretical perspectives on embodiment. A particularly active line of work explores how agents can leverage generative models or internal simulations to anticipate outcomes before acting, as seen in approaches like Autonomous Imagination[3], Robotic Imagination Rearrangement[11], and Imagine Verify Execute[44]. These methods often trade off computational cost against improved safety or sample efficiency. RIG[0] sits squarely within the Integrated Reasoning and Imagination for Generalist Policies branch, aiming to unify symbolic reasoning with imaginative forward modeling in a single policy framework. This contrasts with more modular pipelines in neighboring branches—such as NavCoT[1] in navigation or CubeRobot[5] in manipulation—that may separate reasoning and perception into distinct stages. By targeting generalist policies, RIG[0] aligns closely with recent efforts like Embodied World Models[28] and Unified World Models[34], which also seek to merge predictive simulation with high-level decision-making, yet RIG[0] emphasizes the synergy between reasoning and imagination rather than treating them as independent modules.

Claimed Contributions

End-to-end generalist policy synergizing reasoning and imagination

The authors introduce RIG, a unified end-to-end policy that jointly learns textual reasoning, low-level action control, and visual imagination within a single autoregressive Transformer. This synergy enables the agent to reason about actions and predict their visual outcomes simultaneously, improving sample efficiency and generalization compared to prior methods that treat these capabilities separately.

10 retrieved papers
Progressive data collection strategy for training RIG

The authors develop a multi-stage data pipeline (S0–S4) that progressively enriches existing trajectories with reasoning annotations and reflective reviewing content. This strategy enables training RIG-basic (reasoning without imagination) and RIG-lookahead (reasoning with imagination) by systematically integrating textual rationales and dream-review style trajectories into action-image data.

10 retrieved papers
Test-time scaling through lookahead reasoning

The authors introduce a lookahead mechanism where RIG generates hypothetical dream trajectories by predicting future images, reviews these imagined outcomes, and then produces refined actions. This approach allows the agent to scale inference-time computation by varying the number of lookahead steps, improving decision robustness without additional environment interactions.

10 retrieved papers

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

End-to-end generalist policy synergizing reasoning and imagination

The authors introduce RIG, a unified end-to-end policy that jointly learns textual reasoning, low-level action control, and visual imagination within a single autoregressive Transformer. This synergy enables the agent to reason about actions and predict their visual outcomes simultaneously, improving sample efficiency and generalization compared to prior methods that treat these capabilities separately.

Contribution

Progressive data collection strategy for training RIG

The authors develop a multi-stage data pipeline (S0–S4) that progressively enriches existing trajectories with reasoning annotations and reflective reviewing content. This strategy enables training RIG-basic (reasoning without imagination) and RIG-lookahead (reasoning with imagination) by systematically integrating textual rationales and dream-review style trajectories into action-image data.

Contribution

Test-time scaling through lookahead reasoning

The authors introduce a lookahead mechanism where RIG generates hypothetical dream trajectories by predicting future images, reviews these imagined outcomes, and then produces refined actions. This approach allows the agent to scale inference-time computation by varying the number of lookahead steps, improving decision robustness without additional environment interactions.

Table of Contents