MSD decouples text and visual tokens in the draft model and trains it first on text, then on a gradually increasing mix of visual data, yielding roughly 2x lossless speedups on LLaVA benchmarks.
An image is worth 1/2 tokens after layer 2: Plug-and-play inference acceleration for large vision-language models
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Speculative Decoding Reimagined for Multimodal Large Language Models
MSD decouples text and visual tokens in the draft model and trains it first on text, then on a gradually increasing mix of visual data, yielding roughly 2x lossless speedups on LLaVA benchmarks.