SpatialThinker: Reinforcing 3D Reasoning in Multimodal LLMs via Spatial Rewards
Multimodal large language models (MLLMs) have achieved remarkable progress in vision-language tasks, but they continue to struggle with spatial understanding. Existing spatial MLLMs often rely on explicit 3D inputs or architecture-specific modifications, and remain constrained by large-scale datasets or sparse supervision. To address these limitations, we introduce SpatialThinker, a 3D-aware MLLM trained with RL to integrate structured spatial grounding with multi-step reasoning. The model simulates human-like spatial perception by constructing a scene graph of task-relevant objects and spatial relations, and reasoning towards an answer via dense spatial rewards. SpatialThinker consists of two key contributions: (1) a data synthesis pipeline that generates STVQA-7K, a high-quality spatial VQA dataset, and (2) online RL with a multi-objective dense spatial reward enforcing spatial grounding. SpatialThinker-7B outperforms supervised fine-tuning and the sparse RL baseline on spatial understanding and real-world VQA benchmarks, nearly doubling the base-model gain compared to sparse RL, and surpassing GPT-4o. These results showcase the effectiveness of combining spatial supervision with reward-aligned reasoning in enabling robust 3D spatial understanding with limited data and advancing MLLMs towards human-level visual reasoning.
69140683ac231a5726572002
Hunar Batra
claimed_verified
2025-11-12T12:18:58.669Z
62f5c24eea5bd6b1abc8e151
https://cdn-avatars.huggingface.co/v1/production/uploads/1660273191881-noauth.jpeg
Hunar Batra
hunarbatra
user
hunarbatra
69140683ac231a5726572003
Haoqin Tu
69140683ac231a5726572004
Hardy Chen
69140683ac231a5726572005
Yuanze Lin
69140683ac231a5726572006
Cihang Xie
69140683ac231a5726572007
Ronald Clark
SpatialThinker: Reinforcing 3D Reasoning in Multimodal LLMs via Spatial Rewards
Multimodal large language models (MLLMs) have achieved remarkable progress in vision-language tasks, but they continue to struggle with spatial understanding. Existing spatial MLLMs often rely on explicit 3D inputs or architecture-specific modifications, and remain constrained by large-scale datasets or sparse supervision. To address these limitations, we introduce SpatialThinker, a 3D-aware MLLM trained with RL to integrate structured spatial grounding with multi-step reasoning. The model simulates human-like spatial perception by constructing a scene graph of task-relevant objects and spatial relations, and reasoning towards an answer via dense spatial rewards. SpatialThinker consists of two key contributions: (1) a data synthesis pipeline that generates STVQA-7K, a high-quality spatial VQA dataset, and (2) online RL with a multi-objective dense spatial reward enforcing spatial grounding. SpatialThinker-7B outperforms supervised fine-tuning and the sparse RL baseline on spatial understanding and real-world VQA benchmarks, nearly doubling the base-model gain compared to sparse RL, and surpassing GPT-4o. These results showcase the effectiveness of combining spatial supervision with reward-aligned reasoning in enabling robust 3D spatial understanding with limited data and advancing MLLMs towards human-level visual reasoning.
SpatialThinker, a 3D-aware MLLM trained with RL, enhances spatial understanding by integrating structured spatial grounding and multi-step reasoning, outperforming existing models on spatial VQA and real-world benchmarks.
MLLMs spatial understanding 3D inputs architecture-specific modifications large-scale datasets sparse supervision SpatialThinker scene graph spatial rewards data synthesis pipeline STVQA-7K online RL multi-objective dense spatial reward spatial grounding reward-aligned reasoning visual reasoning