RIG: Synergizing Reasoning and Imagination in End-to-End Generalist
  Policy
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.
67eb544113ca8dcb9ccb991b
Zhonghan Zhao
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2025-07-15T19:12:44.957Z
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Zhonghan Zhao
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OrilinZ
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Wenwei Zhang
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2025-04-01T07:59:04.743Z
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Wenwei Zhang
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ZwwWayne
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Haian Huang
67eb544113ca8dcb9ccb991e
Kuikun Liu
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2025-07-23T08:38:15.212Z
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liu
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Harold-lkk
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Jianfei Gao
admin_assigned
2025-04-01T07:59:34.182Z
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Gao Jianfei
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pppppM
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Gaoang Wang
admin_assigned
2025-04-01T07:59:40.156Z
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Gaoang Wang
user
GaoangWang
67eb544113ca8dcb9ccb9921
Kai Chen
RIG: Synergizing Reasoning and Imagination in End-to-End Generalist
  Policy
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.
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.
Reasoning Imagination Generalist policy RIG data pipeline next image generation joint learning reasoning sample efficiency robustness generalization interoperability test-time scaling