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treeChild: FoldTreeFrame | null;，更多细节参见新收录的资料
。新收录的资料对此有专业解读
Run the live demo · Source code on GitHub。业内人士推荐PDF资料作为进阶阅读
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: