Zero-Shot Vision Encoder Grafting via LLM Surrogates
Authors: name: Kaiyu Yue, name: Vasu Singla, name: Menglin Jia, name: John Kirchenbauer, name: Rifaa Qadri, name: Zikui Cai, name: Abhinav Bhatele, name: Furong Huang, name: Tom Goldstein

Vision language models (VLMs) typically pair a modestly sized vision encoder
with a large language model (LLM), e.g., Llama-70B, making the decoder the
primary computational burden during training. To reduce costs, a potential
promising strategy is to first train the vision encoder using a small language
model before transferring it to the large one. We construct small "surrogate
models" that share the same embedding space and representation language as the
large target LLM by directly inheriting its shallow layers. Vision encoders
trained on the surrogate can then be directly transferred to the larger model,
a process we call zero-shot grafting -- when plugged directly into the
full-size target LLM, the grafted pair surpasses the encoder-surrogate pair
and, on some benchmarks, even performs on par with full decoder training with
the target LLM. Furthermore, our surrogate training approach reduces overall
VLM training costs by ~45% when using Llama-70B as the decoder.