Today’s mainstream LLMs are Transformer-based Next Token Prediction machines: they inject world knowledge by pretraining on massive corpora, then use SFT to reshape their output into a conversational style, and finally, through post-training methods such as RLHF and RLVR, align with human preferences, control safety risks, improve tool-call correctness, enhance generalization, and build long-horizon agentic coding ability.