Freaks City

From Pre-training to Post-training Large language models are usually built in two major stages: Pre-training: The model learns to predict the next token given a prefix, acquiring broad linguistic competence. Post-training: After the model can "speak", we teach it to "think". This stage splits into Supervised Fine-Tuning (S ...

Reinforcement Learning from Human Feeedback (RLHF) aligns language models with human preferences through a multi-stage process. Modern large language model pipelines commonly incorporate RLHF, often combining techniques like Direct Preference Optimization (DPO) followed by Proximal Policy Optimization (PPO). This guide focuses on implementing t ...