title: RIG: Synergizing Reasoning and Imagination in End-to-End Generalist
    Policy
summary: 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 17times 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.
paper_authors._id: 67eb544113ca8dcb9ccb991b
paper_authors.name: Zhonghan Zhao
paper_authors._id: 67eb544113ca8dcb9ccb991c
paper_authors.name: Wenwei Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-01T07:59:04.743Z
paper_authors._id: 64e8505321540e1da3226b54
paper_authors.avatarUrl: /avatars/18958b8406d1ce492b54c1c839f18c54.svg
paper_authors.fullname: Wenwei Zhang
paper_authors.type: user
paper_authors.user: ZwwWayne
paper_authors._id: 67eb544113ca8dcb9ccb991d
paper_authors.name: Haian Huang
paper_authors._id: 67eb544113ca8dcb9ccb991e
paper_authors.name: Kuikun Liu
paper_authors._id: 67eb544113ca8dcb9ccb991f
paper_authors.name: Jianfei Gao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-01T07:59:34.182Z
paper_authors._id: 64070c5c4dc5f2846c925e93
paper_authors.avatarUrl: /avatars/ac2d7c1cd4ecccd6a88b85767c963ec7.svg
paper_authors.fullname: Gao Jianfei
paper_authors.type: user
paper_authors.user: pppppM
paper_authors._id: 67eb544113ca8dcb9ccb9920
paper_authors.name: Gaoang Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-01T07:59:40.156Z
paper_authors._id: 64c9033c5381684d3eaac7f1
paper_authors.avatarUrl: /avatars/07d36ca193826044b0df04e3602b9ef8.svg
paper_authors.fullname: Gaoang Wang
paper_authors.type: user
paper_authors.user: GaoangWang
paper_authors._id: 67eb544113ca8dcb9ccb9921
paper_authors.name: Kai Chen
paper_title: RIG: Synergizing Reasoning and Imagination in End-to-End Generalist
    Policy
paper_summary: 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 17times 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.
paper_ai_summary: The paper presents RIG, an end-to-end agent policy that integrates reasoning and imagination, significantly improving sample efficiency and generalization in complex environments through joint reasoning and action outcome prediction.
paper_ai_keywords: Reasoning Imagination Generalist policy RIG data pipeline next image generation joint learning reasoning sample efficiency robustness generalization interoperability test-time scaling