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

Researchers introduced Filling Before Advancing, a staged post-training method for remote sensing multimodal LLMs.

The approach first fills missing prerequisite capabilities, then fine-tunes models for coastal harbor understanding.

A new dataset and HarborEval benchmark were created to support this scenario-specialized Earth observation task.

Results suggest multi-stage capability building can beat direct fine-tuning when domain data is limited.