RIG: Synergizing Reasoning and Imagination in End-to-End Generalist Policy (2503.24388v1)

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, and thus exhibits more than $17\times$ 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.

Synergizing Reasoning and Imagination in End-to-End Generalist Policies: A Summary of the RIG Framework

The paper entitled "RIG: Synergizing Reasoning and Imagination in End-to-End Generalist Policy," introduces an innovative approach to enhancing embodied agents by integrating reasoning and imagination capabilities within a single unified architecture. This approach, termed RIG, aims to address the limitations in prior work where reasoning and imagination were either models separately or not effectively integrated into an end-to-end learning framework for embodied agents.

Embodied agents operating in complex open-world environments benefit significantly from the ability to reason about their actions and imagine prospective outcomes. Prior approaches often incorporate these faculties individually or through multiple specialized models, which can limit learning efficiency and the generalizability of the policies. By contrast, RIG seeks to synergize these abilities within an end-to-end generalist policy model, thereby improving both sample efficiency and generalization.

To train RIG in an end-to-end fashion, the authors construct a comprehensive data pipeline that progressively combines and enriches data with content involving both imagination and reasoning, drawn from trajectories of existing agents. This strategy facilitates a joint learning process where reasoning and the potential generation of subsequent images are modeled together. Such joint modeling explicitly captures the interactions between reasoning, action, and environmental dynamics, resulting in a remarkable $17\times$ improvement in sample efficiency compared to previous methodologies.

RIG's inference process commences with reasoning about the next action, which then leads to the generation of potential actions and the subsequent prediction of action outcomes. This sequence grants the agent an opportunity to self-review and make corrections based on its imaginative insights prior to executing real actions. Empirical evaluations demonstrate that the synergistic integration of reasoning and imagination enhances the robustness, generalization, and interoperability of the generalist policy. Furthermore, it permits scaling at test time, ultimately boosting overall performance.

The experimental validation conducted in open-world environments like Minecraft showcases RIG's capabilities. It not only surpasses current state-of-the-art results across various benchmarks, such as embodied tasks, image generation, and reasoning tasks, but it also achieves this by training on substantially less data—only 111 hours of videos compared to roughly 2000 hours for previous models.

The paper's contributions are significant for several reasons:

1. Integration of Abilities: RIG is the first framework to leverage end-to-end learning of combined reasoning and imagination, bridging the gap between these functions in a single, generalist policy.
2. Data Efficiency: Through a progressively enriched data pipeline and effective training approaches, RIG achieves high sample efficiency and generalization with substantially less training data.
3. Scalability: RIG supports dynamic lookahead reasoning, enhancing action robustness and reducing the necessity for trial-and-error during inference.

These contributions present new possibilities for embodied agents, offering a pathway towards more versatile and efficient learning models in AI. The implications are broad, potentially impacting applications in robotics, virtual assistants, and other AI-driven systems that require nuanced interaction with complex environments.

The paper speculates on future developments where reasoning and imagination could be further integrated into AI models to address tasks across more diverse domains, potentially transforming approaches to AI system design. The demonstrated improvements in generalization and efficiency highlight the practical advantages of such integration and foreshadow ongoing exploration in this promising direction.