To make our models safer, more helpful, and more aligned, we use an existing technique called reinforcement learning from human feedback (RLHF)⁠. On prompts submitted by our customers to the API,A our labelers provide demonstrations of the desired model behavior, and rank several outputs from our models. We then use this data to fine-tune GPT‑3.

The resulting InstructGPT models are much better at following instructions than GPT‑3. They also make up facts less often, and show small decreases in toxic output generation. Our labelers prefer outputs from our 1.3B InstructGPT model over outputs from a 175B GPT‑3 model, despite having more than 100x fewer parameters. At the same time, we show that we don’t have to compromise on GPT‑3’s capabilities, as measured by our model’s performance on academic NLP evaluations.

These InstructGPT models, which have been in beta on the API for more than a year, are now the default language models accessible on our API.B We believe that fine-tuning language models with humans in the loop is a powerful tool for improving their safety and reliability, and we will continue to push in this direction.

This is the first time our alignment research, which we’ve been pursuing⁠ for several⁠ years⁠,1, 2, 3 has been applied to our product. Our work is also related to recent research that fine-tunes language models to follow instructions using academic NLP datasets, notably FLAN4 and T0.5 A key motivation for our work is to increase helpfulness and truthfulness while mitigating the harms and biases of language models.6, 7, 8, 9, 10 Some of our previous research⁠ in this direction found that we can reduce harmful outputs by fine-tuning on a small curated dataset of human demonstrations.11 Other research has focused on filtering the pre-training dataset,12 safety-specific control tokens,13, 14 or steering model generations.15, 16 We are exploring these ideas and others in our ongoing alignment research.