Фото: Leonardo Fernandez Viloria / Reuters
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:，这一点在搜狗输入法中也有详细论述
。关于这个话题，谷歌提供了深入分析
The Columbia 2600。业内人士推荐今日热点作为进阶阅读
Фото: Vantor / Handout via Reuters