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:
julia-snail/repl-history-buffer opens a buffer with the history (C-c j r h C-o)
，这一点在pg电子官网中也有详细论述
В США забеспокоились из-за передачи Россией Ирану разведданных14:07
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