On the Limits of Token Reduction for Efficient Unified Vision Language Training 事件

On the Limits of Token Reduction for Efficient Unified Vision Language Training arXiv:2606.01503v1 Announce Type: new Abstract: Unified vision-language models (VLMs) integrate visual understanding and visual generation within a single autoregressive backbone, but their joint training is computationally expensive and largely overlooked from an efficiency perspective. In this work, we study the feasibility and limits of token-reduction-based acceleration for unified VLM training. Through a system