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The paradigm of LLMs. Pre-training: LLMs are trained on large scale corpus using autoregressive language model; Instruction Fine-tuning: pre-trained LLMs are fine-tuned on a dataset of human-written demonstrations of the desired output behavior on prompts using supervised learning; RLHF Fine-tuning: a reward model is trained using collected comparison data, then the supervised model is further fine-tuned against the reward model using reinforcement learning algorithm.
