Large Language Models (LLMs) undergo a three-stage training process to become helpful assistants. The initial stage, pretraining, involves predicting the next token on vast internet data, resulting in a knowledgeable but unguided base model. This is followed by supervised fine-tuning (SFT) using curated instruction-response pairs to teach the model to follow commands. The final stage, Reinforcement Learning from Human Feedback (RLHF), uses human preferences to train a reward model and further optimize the LLM for helpfulness, proper formatting, and safety, distinguishing it from base models. AI
IMPACT Understanding LLM training stages clarifies model behavior, alignment challenges, and cost differences between pretraining and fine-tuning.
RANK_REASON The item explains the technical process of training LLMs, including pretraining, SFT, and RLHF. [lever_c_demoted from research: ic=1 ai=1.0]
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