By sharing backbone parameters across parallel branches with task-specific computation allocation, you can improve multimodal model performance without increasing model size or inference latency.
ParVL introduces a framework for scaling multimodal AI models by running multiple parallel vision and language processing branches that share the same core parameters. Instead of making models bigger or slower, it reuses existing components more efficiently and lets different tasks use different amounts of vision vs.