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SpatialOPSD: Self-Distilling Spatial Intelligence from Verified Coding Agent Traces

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Briefings

This study investigates whether MLLMs can internalize spatial reasoning without external tools by using verified agent traces as privileged information. The authors propose SpatialOPSD, an on-policy self-distillation framework that combines repetition masking with unlikelihood regularization to mitigate leakage risks. Experiments show that self-distilling SpatialOPSD outperforms standard SFT and GRPO methods on both spatial and out-of-distribution datasets, demonstrating superior accuracy and generalization.

Key points

• SpatialOPSD is an on-policy self-distillation framework that internalizes spatial reasoning into a standalone MLLM using verified agent traces as privileged information.
• The method uses repetition masking and unlikelihood regularization to prevent privileged-information leakage during the distillation process.

Context

Spatial coding agents improve MLLM spatial reasoning via external tools but incur high inference-time overhead. This research explores internalizing that capability tool-free, using verified traces as privileged information to achieve self-distillation.

Limitations

The study presents experimental results on multiple benchmarks but does not provide specific performance metrics, dataset names, or exact dates for the experiments conducted.

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