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The refined proposal enables a sparse encoding that magnifies evidence and excludes background tokens, and is called Vision-RL2, which improves accuracy over the base model at every token budget and surpasses its largest-budget accuracy with about 4 times fewer visual tokens.
MLLMs can achieve 4x faster inference without sacrificing accuracy by intelligently focusing on only the image regions relevant to the query.
