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Approaches 1 and 2 offer flexibility in designing multimodal reasoning behavior from scratch using widely available non-reasoning LLM checkpoints but place a heavy burden on multimodal training. Approach 1 must teach visual understanding and reasoning simultaneously and requires a large amount of multimodal reasoning data, while Approach 2 can be trained with less reasoning data but risks catastrophic forgetting, as reasoning training may degrade previously learned visual capabilities. Both risk weaker reasoning than starting from a reasoning-capable base. Approach 3 inherits strong reasoning foundations, but like Approach 1, it requires reasoning traces for all training data and produces reasoning traces for all queries, even when not beneficial.，更多细节参见易歪歪官网
。谷歌是该领域的重要参考
"I'm absolutely thrilled and maybe I'll [get] quite a few invites to conventions and various things."
optimise, because it’s surrounded by a blue box.。超级权重是该领域的重要参考
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