SpatialLadder: Progressive Training for Spatial Reasoning in Vision-Language Models

• URL: http://arxiv.org/abs/2510.08531v1
• Date: Thu, 09 Oct 2025 17:50:54 GMT
• Title: SpatialLadder: Progressive Training for Spatial Reasoning in Vision-Language Models
• Authors: Hongxing Li, Dingming Li, Zixuan Wang, Yuchen Yan, Hang Wu, Wenqi Zhang, Yongliang Shen, Weiming Lu, Jun Xiao, Yueting Zhuang,
• Abstract summary: We present a comprehensive methodology for building spatial intelligence progressively.<n>We introduce SpatialLadder-26k, a multimodal dataset containing 26,610 samples spanning object localization, single image, multi-view, and video spatial reasoning tasks.<n>We design a three-stage progressive training framework that establishes spatial perception through object localization, develops spatial understanding through multi-dimensional spatial tasks, and strengthens complex reasoning via reinforcement learning with verifiable rewards.
• Score: 73.19077622773075
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spatial reasoning remains a fundamental challenge for Vision-Language Models (VLMs), with current approaches struggling to achieve robust performance despite recent advances. We identify that this limitation stems from a critical gap: existing methods attempt to learn spatial reasoning directly without establishing the hierarchical foundations of perception and understanding. To address this challenge, we present a comprehensive methodology for building spatial intelligence progressively. We introduce SpatialLadder-26k, a multimodal dataset containing 26,610 samples spanning object localization, single image, multi-view, and video spatial reasoning tasks, constructed through a standardized pipeline that ensures systematic coverage across modalities. Building on this dataset, we design a three-stage progressive training framework that (1) establishes spatial perception through object localization, (2) develops spatial understanding through multi-dimensional spatial tasks, and (3) strengthens complex reasoning via reinforcement learning with verifiable rewards. This approach yields SpatialLadder, a 3B-parameter model that achieves state-of-the-art performance on spatial reasoning benchmarks, with 23.4% average improvement over the base model, surpassing GPT-4o by 20.8% and Gemini-2.0-Flash by 10.1%. Notably, SpatialLadder maintains strong generalization with 7.2% improvement on out-of-domain benchmarks, demonstrating that progressive training from perception to reasoning is essential for robust spatial intelligence.

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