  Existing vision-language models (VLMs) mostly rely on vision encoders to
extract visual features followed by large language models (LLMs) for
visual-language tasks. However, the vision encoders set a strong inductive bias
in abstracting visual representation, e.g., resolution, aspect ratio, and
semantic priors, which could impede the flexibility and efficiency of the VLMs.
Training pure VLMs that accept the seamless vision and language inputs, i.e.,
without vision encoders, remains challenging and rarely explored. Empirical
observations reveal that direct training without encoders results in slow
convergence and large performance gaps. In this work, we bridge the gap between
encoder-based and encoder-free models, and present a simple yet effective
training recipe towards pure VLMs. Specifically, we unveil the key aspects of
training encoder-free VLMs efficiently via thorough experiments: (1) Bridging
vision-language representation inside one unified decoder; (2) Enhancing visual
recognition capability via extra supervision. With these strategies, we launch
EVE, an encoder-free vision-language model that can be trained and forwarded
efficiently. Notably, solely utilizing 35M publicly accessible data, EVE can
impressively rival the encoder-based VLMs of similar capacities across multiple
vision-language benchmarks. It significantly outperforms the counterpart
Fuyu-8B with mysterious training procedures and undisclosed training data. We
believe that EVE provides a transparent and efficient route for developing a
pure decoder-only architecture across modalities. Our code and models are
publicly available at: https://github.com/baaivision/EVE.
