SpatialDreamer: Incentivizing Spatial Reasoning via Active Mental Imagery
Meng Cao, Xingyu Li, Xue Liu, Ian Reid, Xiaodan Liang

TL;DR
SpatialDreamer introduces a reinforcement learning framework that enhances spatial reasoning in multimodal models by enabling active exploration, visual imagination, and evidence-based reasoning, significantly improving performance on complex benchmarks.
Contribution
It presents SpatialDreamer, a novel RL-based approach with GeoPO for fine-grained rewards, advancing active mental imagery in spatial reasoning tasks.
Findings
Achieves state-of-the-art results on multiple benchmarks
Demonstrates effective active spatial mental simulation
Outperforms passive observation methods
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMultimodal Machine Learning Applications · Constraint Satisfaction and Optimization · Spatial Cognition and Navigation
