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The article explains how pretrained language models are aligned through two stages: supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF). It details SFT as training on human-written prompt–response pairs using backpropagation to make models more helpful, polite, and responsive, but notes SFT datasets are much smaller than pretraining corpora and can cause overfitting and poor generalization. The piece argues RLHF is needed because scaling supervised datasets by hand
SFT and RLHF are the practical methods used to align large pretrained models to human preferences, affecting model safety, usefulness, and deployment risk. Tech teams need to understand tradeoffs in data, overfitting, and how reward models drive final behavior.
Dossier last updated: 2026-05-27 14:31:46
The article explains how a trained reward model is used to improve an original language model via reinforcement learning from human feedback (RLHF). Starting with new prompts outside supervised fine-tuning data, the base model generates responses that the reward model scores; low scores signal unhelpful or misaligned outputs. Those reward signals guide RL training so the model learns to produce more polite, useful, and aligned responses, which then receive higher rewards. The piece concludes that RLHF yields a trained, aligned model better matching human preferences and notes the series will continue with new topics. It also briefly promotes an installation tool called Installerpedia.
OpenAI’s 2022 RLHF approach trains a reward model without predefining target reward values by using a loss built from the reward difference between preferred and less-preferred responses. The model computes Reward_better - Reward_worse, passes that difference through a sigmoid (squashing values to 0–1), applies a log, and multiplies by -1 to form a loss that gradient-based optimizers can minimize. This encourages the reward model to assign higher scores to preferred outputs organically, rather than forcing absolute reward defaults. Once trained, the reward model supervises further policy optimization of the original model beyond supervised fine-tuning. The article previews using the reward model to continue training the main model.
The article explains how to convert supervised fine-tuned language models into reward models for Reinforcement Learning from Human Feedback (RLHF). It outlines taking a copy of a fine-tuned model, removing its unembedding (token prediction) layer, and replacing it with a single scalar output so the model predicts a reward score for full responses. The reward model is then trained on collected human preference pairs: preferred responses get higher reward targets, less preferred responses get lower or negative rewards. Over iterations the reward model internalizes human preference patterns and can guide downstream policy optimization. The piece is introductory and focused on the model modification and training step in RLHF.
The article explains how pretrained language models are aligned through two stages: supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF). It details SFT as training on human-written prompt–response pairs using backpropagation to make models more helpful, polite, and responsive, but notes SFT datasets are much smaller than pretraining corpora and can cause overfitting and poor generalization. The piece argues RLHF is needed because scaling supervised datasets by hand is costly; RLHF can improve model behavior on unseen prompts without enormous manual labeling. The author signals a follow-up article will dive into RLHF. An ad for Installerpedia appears at the end.