RadarVL: From Model-Generated Candidates to Expert-Verified Supervision for Domain-Specialized VLMs

Specializing general-purpose vision-language models (VLMs) to scientific visual tasks with high domain expertise requirements relies on large-scale, high-quality, and reliable domain supervision. However, constructing such supervision entirely through manual annotation by domain experts is costly, while directly using descriptions generated by strong VLMs as training labels may introduce erroneous supervision that is inconsistent with the original observations. To address this challenge, we focus on weather-radar understanding, an interdisciplinary task that requires professional meteorological knowledge, temporal visual reasoning, and multimodal interpretation, and propose an expert-verified synthetic supervision approach. Based on this approach, we construct Radar-9K, containing 9,089 expert-verified convective events and 94,825 MRMS composite-reflectivity frames, and further specialize an 8B VLM into the weather-radar vision-language model RadarVL-8B. Experiments show that verified supervision substantially improves radar-domain understanding. On the MRMS test set, RadarVL-8B improves the score from 64.8 to 77.1. On unseen European OPERA radar observations, its expert score improves from 61.7 to 71.5 without additional fine-tuning. These results demonstrate that candidate annotations generated by strong VLMs can effectively support the specialization of weather-radar models, while reliability verification is critical for obtaining high-quality supervision and robust downstream performance.