SCOUT: Unlocking Enhanced Spatial Reasoning via Structured Chain-of-Thought and Multi-Objective Process Reward

2026-08-12

AI Summary

This work addresses the limitations of existing vision-language models in robust spatial reasoning, particularly their inadequate credit assignment and lack of depth perception. To overcome these challenges, the authors propose SCOUT, a novel framework that integrates structured chain-of-thought (CoT) reasoning with explicit 3D environmental modeling and a reinforcement learning algorithm featuring multi-target process rewards to enable fine-grained credit assignment, complemented by a tailored advantage estimation method. Additionally, they introduce SCOUT-24k, the first structured dataset for spatial reasoning. Experimental results demonstrate that SCOUT-3B achieves performance gains of 16.85% and 6.3% on general and complex spatial tasks, respectively, while SCOUT-7B surpasses GPT-4o by 4.28% and exhibits strong generalization across multi-image and video scenarios.

Abstract

Existing Vision-Language Models (VLMs) exhibits a critical bottleneck in robust spatial reasoning. Recent reinforcement learning (RL) methods aim to close this gap with verifiable outcomes, yet they suffer from poor credit assignment across intermediate reasoning steps. Concurrently, structured reasoning approaches overlook the critical depth perception necessary for comprehensive 3D understanding. To address these challenges, we propose SCOUT (Structured Chain-Of-Thought Utilizing Process-Supervised RL Training). Specifically, we design a structured Chain-of-Thought (CoT) framework that explicitly models 3D environmental perception to ensure robust spatial understanding and reasoning. Furthermore, we introduce a novel RL algorithm featuring multi-objective process rewards and a tailored advantage estimation method, facilitating fine-grained credit assignment across distinct segments of the reasoning trajectory. To support our framework, we develop SCOUT-24k, a structured spatial reasoning CoT dataset synthesized through a customized pipeline. Extensive evaluations demonstrate that SCOUT-3B improves upon baseline models by 16.85% and 6.3% on general spatial benchmarks and complex spatial reasoning tasks respectively. Notably, our larger SCOUT-7B even outperforms GPT-4o by a margin of 4.28%. Moreover, despite being trained exclusively on single image, SCOUT-7B exhibits robust out-of-domain generalization to multi-image and video scenarios. These empirical results render SCOUT as a critical step towards next generation of spatially-aware VLMs.

Problem

• spatial reasoning
• Vision-Language Models
• 3D understanding
• credit assignment
• depth perception

Innovation

• Structured Chain-of-Thought
• Multi-Objective Process Reward
• 3D Spatial Reasoning
• Process-Supervised RL
• Vision-Language Models