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LVLMs can achieve SOTA visual reasoning by learning to "see" in a way that optimizes for reasoning, even if it means deviating from strict geometric accuracy.
Achieve up to 12x greater sample efficiency in reasoning tasks by relaxing strict imitation constraints in on-policy distillation, enabling smaller models to match the performance of much larger ones.
