Training language models to follow instructions with human feedback

Summary

This paper from OpenAI introduces InstructGPT, a language model trained to follow instructions. The core innovation lies in leveraging human feedback to fine-tune a pre-trained language model. The process involves three stages: (1) collecting a dataset of instruction-following demonstrations, where human labelers write demonstrations of how they would respond to instructions; (2) training a supervised learning model on this dataset; and (3) training a reward model based on human comparisons of model outputs, which is then used to fine-tune the language model using reinforcement learning. This approach leads to models that are better at following instructions, producing more helpful, honest, and harmless outputs compared to models fine-tuned using only the original language modeling objective or with prompt engineering. The paper highlights the effectiveness of this human feedback-based training paradigm for aligning language models with human preferences and reducing undesirable behaviors. The research provides insights into the impact of instruction fine-tuning and the use of reward models to achieve better alignment with human expectations for language model behavior.

Key Takeaways

1. InstructGPT utilizes a three-stage training process involving supervised learning and reinforcement learning with human feedback.
2. Human feedback in the form of comparisons of model outputs is crucial for defining a reward model that guides model behavior.
3. Fine-tuning language models on instruction-following data significantly improves their ability to follow instructions and generate more desirable responses.
4. The use of human feedback leads to language models that are more aligned with human preferences in terms of helpfulness, honesty, and harmlessness.