---
title: "SCOUT: Unlocking Enhanced Spatial Reasoning via Structured Chain-of-Thought and Multi-Objective Process Reward"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.12220v1"
permalink: "https://feed.astralor.com/items/itm_944207d271cea00ea0c4cc13"
published: "2026-08-12T16:14:25.000Z"
score: 76
channel: "ai"
tags: ["SCOUT","Reinforcement Learning","Chain-of-Thought","Spatial Reasoning","Vision-Language Model"]
---

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

## AI commentary

> Spatial reasoning is a key leap for VLMs to move from 'seeing' to 'understanding.' SCOUT combines structured CoT with multi-objective rewards, making it especially relevant for teams working on embodied AI, autonomous driving, or visual navigation—consider examining its dataset and training details for potential transfer to your own scenarios.

## At a glance

Existing Vision-Language Models exhibit a critical bottleneck in robust spatial reasoning. Recent RL methods close the gap with verifiable outcomes, but suffer from poor credit assignment across intermediate steps, and structured reasoning approaches overlook depth perception. SCOUT introduces a structured Chain-of-Thought framework that explicitly models 3D environmental perception, plus a novel RL algorithm with multi-objective process rewards and tailored advantage estimation for fine-grained credit assignment. The authors also contribute SCOUT-24k, a structured spatial reasoning CoT dataset. Experiments show SCOUT-3B improves by 16.85% and 6.3% on general spatial benchmarks and complex spatial reasoning tasks, while SCOUT-7B surpasses GPT-4o by 4.28%, and generalizes to multi-image and video scenarios.

## Key points

- Proposes the SCOUT framework with a structured Chain-of-Thought explicitly modeling 3D environmental perception.
- Introduces a novel RL algorithm with multi-objective process rewards and tailored advantage estimation for fine-grained credit assignment.
- Builds SCOUT-24k, a structured spatial reasoning CoT dataset.
- SCOUT-7B surpasses GPT-4o by 4.28% on spatial benchmarks and generalizes to multi-image and video inputs.

## Full text

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.

---

Verify against the original before citing; curated by Astral Feed: https://feed.astralor.com/items/itm_944207d271cea00ea0c4cc13