This paper presents a method for aligning large language models (LLMs) with human intent by fine-tuning with human feedback. The approach involves three main steps: supervised fine-tuning (SFT), reward model (RM) training, and reinforcement learning from human feedback (RLHF). The resulting models, called InstructGPT, are trained using human-labeled data and show significant improvements in truthfulness, reduced toxicity, and better alignment with user intent compared to the base GPT-3 model. The SFT step involves training a GPT-3 model on a dataset of human-written demonstrations of desired behavior. The RM step trains a model to predict which of two model outputs is preferred by humans. The RLHF step uses the RM as a reward function to fine-tune the model using the PPO algorithm. This process results in models that are more aligned with human preferences and perform better on a range of tasks. In human evaluations, InstructGPT models outperform GPT-3 models, even when they have significantly fewer parameters. InstructGPT models also show improvements in truthfulness and reductions in toxic output generation, while maintaining performance on public NLP datasets. However, they still make simple mistakes, such as failing to follow instructions or generating toxic outputs when prompted. The paper also discusses the challenges of aligning language models with human intent, including the risk of performance regressions on public NLP datasets. The authors propose a solution by mixing pretraining updates with PPO updates, which helps to mitigate these regressions. They also discuss the limitations of their approach, including the potential for bias and the need for further research to improve the safety and reliability of language models. Overall, the results indicate that fine-tuning large language models using human feedback is a promising direction for aligning them with human intent. The InstructGPT models show significant improvements in alignment with human preferences and perform better on a range of tasks compared to the base GPT-3 model. However, further research is needed to improve the safety and reliability of language models.This paper presents a method for aligning large language models (LLMs) with human intent by fine-tuning with human feedback. The approach involves three main steps: supervised fine-tuning (SFT), reward model (RM) training, and reinforcement learning from human feedback (RLHF). The resulting models, called InstructGPT, are trained using human-labeled data and show significant improvements in truthfulness, reduced toxicity, and better alignment with user intent compared to the base GPT-3 model. The SFT step involves training a GPT-3 model on a dataset of human-written demonstrations of desired behavior. The RM step trains a model to predict which of two model outputs is preferred by humans. The RLHF step uses the RM as a reward function to fine-tune the model using the PPO algorithm. This process results in models that are more aligned with human preferences and perform better on a range of tasks. In human evaluations, InstructGPT models outperform GPT-3 models, even when they have significantly fewer parameters. InstructGPT models also show improvements in truthfulness and reductions in toxic output generation, while maintaining performance on public NLP datasets. However, they still make simple mistakes, such as failing to follow instructions or generating toxic outputs when prompted. The paper also discusses the challenges of aligning language models with human intent, including the risk of performance regressions on public NLP datasets. The authors propose a solution by mixing pretraining updates with PPO updates, which helps to mitigate these regressions. They also discuss the limitations of their approach, including the potential for bias and the need for further research to improve the safety and reliability of language models. Overall, the results indicate that fine-tuning large language models using human feedback is a promising direction for aligning them with human intent. The InstructGPT models show significant improvements in alignment with human preferences and perform better on a range of tasks compared to the base GPT-3 model. However, further research is needed to improve the safety and reliability of language models.