Autoregressive To Diffusion Vlms
- Source
- runway.com
- Author
- Runway
- Date
Context
Runway describes a way to make an image-understanding model generate several pieces of its written answer at once. Its A2D-VL research adapts an existing Qwen2.5-VL model, progressively training it to fill missing text within eight-token blocks. Earlier completed blocks stay available as and their computed values can be cached exactly, so the model can continue producing longer answers without recomputing them. This changes the speed choice available at generation time. A high confidence threshold permits simultaneous predictions only when the model is sufficiently certain; lowering it allows more parallel output while risking lower quality. The research article presents this as a tunable speed-quality tradeoff obtained by adapting a pretrained model.
- context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
- attention — The mechanism that lets a model weigh which earlier words matter for the word it's currently processing — the core operation of a transformer.
- LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
- pretraining — The first, biggest phase of building a model: training it on enormous amounts of text so it learns language, facts, and reasoning in general.
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