# Spatial-Interactor：通过与环境交互训练 VLM 的空间推理能力

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-09-19 08:00
- AIHOT 分数：42
- AIHOT 链接：https://aihot.news/items/cmuf32nqp08h0rood6vvo6tcm
- 原文链接：https://arxiv.org/abs/2609.23038

## AI 摘要

Spatial-Interactor 框架通过交互轨迹训练 VLM 建模物理世界状态转换，采用 L1 被动世界状态、L2 主动自身状态、L3 长时程交互轨迹三级课程，并构建了 LSI-108K 数据集。

## 正文

Spatial reasoning is essential for vision-language models (VLMs) to understand and act in the physical world. Reasoning in dynamic environments requires VLMs to perceive local state transitions caused by object motion and viewpoint changes and integrate them over long trajectories to maintain an updated spatial state, yet existing VLMs remain limited in both capabilities. Current spatial training primarily focuses on static questions about object attributes and spatial relations, providing limited direct supervision for state transitions; in contrast, interaction trajectories naturally connect a preceding observation, an action, and a subsequent observation, offering direct supervision for local state transitions, while complete trajectories reveal dependencies among consecutive transitions.

We therefore introduce Spatial-Interactor, a framework that trains VLMs to model physical-world state transitions through interaction, organizing this learning process into a three-level curriculum covering L1 passive world-state transitions, L2 active self-state transitions, and L3 long-horizon interaction trajectories. Accordingly, we construct the Learning from Spatial Interaction dataset (LSI-108K) from simulated and real interaction trajectories, with tasks aligned with the objective of each level. Our two-stage training strategy applies Supervised Fine-Tuning (SFT) to L1 and L2 for local transition modeling, and On-Policy Distillation (OPD) then uses privileged self-distillation: a teacher branch given segment-level transition descriptions supervises the student's on-policy CoT, helping the student learn to integrate consecutive transitions over L3 long trajectories.

Experiments across multiple VLMs and spatial benchmarks show consistent gains in local transition modeling and long-horizon integration.