SpatialDreamer: Incentivizing Spatial Reasoning via Active Mental Imagery

• URL: http://arxiv.org/abs/2512.07733v1
• Date: Mon, 08 Dec 2025 17:20:50 GMT
• Title: SpatialDreamer: Incentivizing Spatial Reasoning via Active Mental Imagery
• Authors: Meng Cao, Xingyu Li, Xue Liu, Ian Reid, Xiaodan Liang,
• Abstract summary: SpatialDreamer is a reinforcement learning framework that enables spatial reasoning through a closedloop process of active exploration.<n>GeoPO introduces tree-structured sampling and step-level reward estimation with consistency geometric constraints.
• Score: 64.67498968405327
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Despite advancements in Multi-modal Large Language Models (MLLMs) for scene understanding, their performance on complex spatial reasoning tasks requiring mental simulation remains significantly limited. Current methods often rely on passive observation of spatial data, failing to internalize an active mental imagery process. To bridge this gap, we propose SpatialDreamer, a reinforcement learning framework that enables spatial reasoning through a closedloop process of active exploration, visual imagination via a world model, and evidence-grounded reasoning. To address the lack of fine-grained reward supervision in longhorizontal reasoning tasks, we propose Geometric Policy Optimization (GeoPO), which introduces tree-structured sampling and step-level reward estimation with geometric consistency constraints. Extensive experiments demonstrate that SpatialDreamer delivers highly competitive results across multiple challenging benchmarks, signifying a critical advancement in human-like active spatial mental simulation for MLLMs.

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