ParVL: Parallel Scaling and Expandable Compute Allocation for Multimodal LLMs
ParVL reuses shared vision and language backbones across parallel branches, letting multimodal systems shift extra compute between modalities without adding separate backbone parameters.
ParVL adds parallel vision and language branches that reuse the same ViT and LLM backbones, with branch-specific prefixes. The model was fine-tuned end to end on **roughly 13B tokens**.
For multimodal agents, treat vision-versus-language compute as a task-level tuning choice rather than a fixed architectural ratio. The framework offers a way to spend more compute without duplicating backbone parameters.
ParVL adds parallel vision and language branches that reuse the same ViT and LLM backbones, with branch-specific prefixes. The model was fine-tuned end to end on **roughly 13B tokens**. For multimodal agents, treat vision-versus-language compute as a task-level tuning choice rather than a fixed architectural ratio. The framework offers a way to spend more compute without duplicating backbone parameters. ParVL beat same-recipe single-branch baselines overall, but the **best allocation varied by task**. The abstract provides no latency, memory, or benchmark-level figures for judging deployment tradeoffs.
This adds branch-level compute allocation as a multimodal scaling lever: shared backbones can support different vision-language compute mixes without duplicating their parameters. It confirms that compute should be tuned by task, while narrowing adoption claims because the available evidence does not reveal whether the quality gains justify latency or memory costs in deployment.