Language-only reasoning models are typically created through supervised fine-tuning (SFT) or reinforcement learning (RL): SFT is simpler but requires large amounts of expensive reasoning trace data, while RL reduces data requirements at the cost of significantly increased training complexity and compute. Multimodal reasoning models follow a similar process, but the design space is more complex. With a mid-fusion architecture, the first decision is whether the base language model is itself a reasoning or non-reasoning model. This leads to several possible training pipelines:
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对于弄虚作假、好大喜功、光说不练、花拳绣腿等政绩观扭曲错位问题，习近平总书记多次提出明确批评，教育引导广大党员干部沉下心来踏实干，“一步一个脚印、稳扎稳打向前走”。
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