RAVEL: training 210 MLLMs to build a strong baseline for picking vision encoders
jiqizhixin · x · 2026-10-26
The vision encoder is core to every multimodal LLM, yet swapping encoders can dramatically change downstream performance even with the same LLM backbone, and end-to-end training of each candidate is prohibitively expensive. Researchers from SJTU, Nanjing University and Fudan propose RAVEL (ArXiv 2610.05413) as a strong baseline for evaluating vision encoders.
- Existing lightweight predictors are unimodal (image classification scores) or cross-modal (vision-language alignment); intuition favors cross-modal
- After training and evaluating 210 MLLMs, they found traditional cross-modal methods degrade as candidate diversity grows, even falling behind unimodal ones
- RAVEL measures cross-modal nearest-neighbor consistency with two simple but effective components
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