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.
李 “대통령·집권세력 됐다고 마음대로 해선 안 돼…권한만큼 책임 커”
。新收录的资料是该领域的重要参考
莘莘学子蓬勃向上的青春身影，是我国一体推进教育科技人才发展的鲜活注脚。科技创新靠人才，人才培养靠教育，教育、科技、人才内在一致、相互支撑。，推荐阅读新收录的资料获取更多信息
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