Filling Before Advancing: Capability-Gap-Driven Post-Training for Scenario-Specialized Remote Sensing MLLMs

2026-07-14 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Remote Sensing · Depth: Expert, extended

Summary

Remote sensing multimodal large language models (RS-MLLMs) struggle with fine-grained scenario specialization due to scarce high-quality data and incomplete capability coverage. The "filling before advancing" (FBA) post-training method addresses this by progressively closing capability gaps. Instantiated for coastal harbor understanding, FBA uses CPRS, a three-layer supervision dataset, across three ordered stages: RS semantic anchoring, domain-bridge convergence, and evidence-grounded scenario tuning. FBA significantly improves performance on the new eight-track HarborEval diagnostic benchmark, increasing scores from 57.95 with Direct-SFT to 70.29 on LLaVA-v1.5, and from 81.09 to 83.37 on Qwen3-VL, outperforming alternative post-training routes and existing RS-MLLMs.

Key takeaway

For Machine Learning Engineers adapting RS-MLLMs to fine-grained scenarios with limited data, consider a staged, capability-gap-driven post-training approach like FBA. This method progressively builds foundational and domain-specific capabilities. It significantly improves model performance and robustness compared to direct fine-tuning, as demonstrated by the 70.29 score on HarborEval for LLaVA-v1.5. You should structure your training data and process to sequentially address prerequisite skills before final scenario specialization.

Key insights

Scenario-specialized RS-MLLM adaptation benefits from progressive capability-gap-driven post-training.

Principles

• Sequential capability gap filling is more effective than single-stage fine-tuning.
• Supervision data should be organized by distinct capability roles.
• Intermediate bridging domains enhance multi-source understanding.

Method

FBA involves three ordered stages: RS semantic anchoring (D1), domain-bridge convergence (D2) using SMT distillation, and evidence-grounded scenario tuning (D3). Each stage builds on the previous one.

In practice

• Construct multi-layer datasets for staged training.
• Develop diagnostic benchmarks for scenario-specific evaluation.
• Use multi-teacher distillation for high-quality instruction data.

Topics

• Remote Sensing MLLMs
• Post-Training Adaptation
• Capability Gap Analysis
• Coastal Harbor Understanding
• Multimodal Data Curation
• Diagnostic Benchmarking

Code references

• Z0ngL1ng/filling-before-advancing

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.