The success of ChatGPT is largely due to the new training paradigm it adopts - reinforcement learning with human feedback (RLHF). RLHF is a reinforcement learning method that combines reinforcement learning with human feedback. By using feedback provided by humans to guide the behavior of intelligent systems, it can learn tasks more efficiently and quickly.
In ChatGPT’s training, human feedback is incorporated into the model’s learning process. ChatGPT is first pre-trained on a large-scale text dataset and then fine-tuned through human interaction. In this process, feedback from human users is used to optimize the model’s output, allowing the model to better understand human intentions and generate text that is more in line with human expectations.
The adoption of this training paradigm makes ChatGPT perform even better when processing natural language tasks, such as dialogue generation, text summarization, semantic understanding, etc. At the same time, because it can learn human preferences and habits, the text generated by ChatGPT is also more in line with human language habits and logic.
The training process of RLHF can be broken down into the following three core steps:
Step1: Pre-train language model
In this stage, the model uses conventional supervised learning methods to learn from large amounts of labeled data. The goal of this stage is to enable the model to understand and generate text as accurately as possible.
Step2: Collect data and train reward model
At this stage, the model generates some text and then gets feedback from humans. This feedback can be ratings on some specific properties of the text, or suggestions for modifications to the text. The purpose of this stage is to let the model gradually learn to generate text that meets human expectations and requirements.
Step3: Use reinforcement learning to fine-tune the language model
The model uses reinforcement learning algorithms to optimize the way it generates text. During this phase, the model continuously generates text and receives feedback from human providers (this is called reward). The goal of the model is to maximize the total return from these rewards. The goal of this stage is to enable the model to adjust the way it generates text based on feedback and rewards from human providers, thereby maximizing the quality of the text it generates.
How to optimize RLHF ?
RLHF mainly performs optimization iterations in the following two ways:
Iterative optimization strategy: RLHF adopts an iterative optimization strategy to improve the performance of large models. It is first initialized using a pre-trained model and then iteratively iterates the training and fine-tuning process. In each iteration, it uses the fine-tuned model to generate new labels and uses these new labels to update the model's weights. This process is repeated until model performance reaches a satisfactory level.
Contextual information: RLHF optimizes the performance of large models by leveraging contextual information. It enhances the expressive ability and generalization ability of the model by introducing contextual information. Specifically, it can use external knowledge base or contextual information to enrich input data. For example, in text classification tasks, it can integrate background knowledge beyond the article to improve the model's ability to understand the text.
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