Behind the Scenes of Neural Networks: The Full Cycle of Training a Language AI
Summary
This article walks through the full pipeline used to train modern language AI models. It covers supervised fine-tuning, instruction tuning, RLHF, reward models, PPO, DPO, and newer approaches such as RLAIF and Constitutional AI. It also explains architectural ideas like Mixture of Experts and newer inference-time scaling methods. The piece uses examples from systems such as GPT-4, ChatGPT, Mixtral, o1, and DeepSeek-R1 to show how these techniques appear in practice.
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