V-REX: Efficient Specialist VLM Training for Veterinary X-Rays

2026-08-20

AI Summary

本文针对兽医X光片诊断问题，通过重新设计VLM训练流程，提出更高效的训练方法，不依赖大规模基础模型即实现优越性能。

Abstract

While generalist VLMs are expensive to train, creating domain experts is widely assumed to require fine-tuning increasingly large foundation models. We show that, in veterinary radiology, this assumption is misguided. By rethinking the entire VLM pipeline - from text tokenisation and pre-training to grounding and inference - we demonstrate that careful engineering can yield models that outperform much larger foundation models from scratch, without relying on any other data. Our approach introduces new strategies for generative pre-training and grounding that improve training efficiency, increasing data utilisation and downstream performance. Using only a fraction of the parameters, data, and compute of contemporary generalist models, we develop the first VLM capable of generating diagnostic reports for veterinary radiographs, surpassing open foundation models on this task by significant margin.

Problem

• VLM
• veterinary radiology
• fine-tuning
• foundation models
• training efficiency

Innovation

• Efficient VLM Training
• Veterinary Radiology
• Generative Pre-training
• Data Utilization
• Diagnostic Report Generation