Learning Visual Spatial Planning from Symbolic State via Modality-Gap-Aware Self-Distillation
While Vision-Language Models excel at general multimodal understanding, they still struggle with visual spatial planning. We attribute this limitation to a perception--reasoning modality gap. Visual planning requires models to infer latent state structures from pixels and then reason over the recovered structure to produce valid actions, whereas symbolic planning directly leverages explicit representation. This discrepancy introduces two sequential bottlenecks: visual state recovery at the perception stage and multi-step planning at the reasoning stage. To address this, we propose MGSD, a two-stage modality-gap-aware self-distillation framework. First, a cold-start grounding stage establi...
| Authors | Haocheng Luo, Jiahui Liu, Ruicheng Zhang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 4 June 2026 |
| Last updated at source | 7 August 2026 |
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