What is reinforcement learning from human feedback (RLHF)?
Reinforcement learning from human feedback (RLHF) is a machine learning approach that combines reinforcement learning techniques, such as rewards and comparisons, with human guidance to train an artificial intelligence (AI) agent.
Machine learning is a vital component of AI. Machine learning trains the AI agent on a particular function by running billions of calculations and learning from them. The whole task is faster than human training due to its automation.
There are times when human feedback is vital to fine-tune an interactive or generative AI, such as a chatbot. Using human feedback for generated text can better optimize the model and make it more efficient, logical and helpful. In RLHF, human testers and users provide direct feedback to optimize the language model more accurately than self-training alone. RLHF is primarily used in natural language processing (NLP) for AI agent understanding in applications such as chatbots and conversational agents, text to speech and summarization.
In regular reinforcement learning, AI agents learn from their actions through a reward function. But the problem is the agent is teaching itself. The rewards are often not easy to define or measure, especially with complex tasks such as NLP. The result is an easily confused chatbot that makes no sense to the user.
This article is part of
The goal of RLHF is to train language models that generate text that is both engaging and factually accurate. It does this by first creating a reward model to predict how humans will rate the quality of text generated by the language model through human feedback, which is then used to train a machine learning model that can predict the human ratings of the text.
Next, it performs language model fine-tuning by using the reward model, where the language model is then rewarded for generating text that is rated highly by the reward model.
It also enables the model to reject questions that are outside the scope of the request. For example, models often refuse to generate any content that advocates violence or is racist, sexist or homophobic.
One example of a model that uses RLHF is OpenAI's ChatGPT.
How does ChatGPT use RLHF?
ChatGPT is a generative AI tool that creates new content, such as chat and conversation, based on prompts. A successful generative AI application should read and sound like a natural human conversation. This means NLP is necessary for the AI agent to understand how human language is spoken and written.
Because ChatGPT generates conversational, real-life answers for the person making the query, it uses RLHF. ChatGPT uses large language models (LLMs) that are trained on a massive amount of data to predict the next word to form a sentence.
But LLMs have limitations and may not fully understand the user request. The question may be too open-ended, or the person may not be clear enough in their instructions. To teach ChatGPT how to create dialogue in a human style of conversation, it was trained using RLHF so the AI learns human expectations.
Training the LLM this way is significant because it goes beyond training it to predict the next word and helps construct an entire coherent sentence. This is what sets ChatGPT apart from a simple chatbot, which typically provides a pre-written, canned answer to answer a question. ChatGPT was specifically trained through human interaction to understand the intent of the question and provide the most natural-sounding and helpful answers.
How does RLHF work?
RLHF training is done in three phases:
- Initial phase. The first phase involves selecting an existing model as the main model to determine and label correct behavior. Using a pre-trained model is a timesaver due to the amount of data required for training.
- Human feedback. After training the initial model, human testers provide input on performance. Human trainers provide a quality or accuracy score to various model-generated outputs. The system then evaluates its performance based on human feedback to create rewards for reinforcement learning.
- Reinforcement learning. The reward model is fine-tuned with outputs from the main model and receives a quality score from testers. The main model uses this feedback to improve its performance on future tasks.
RLHF is an iterative process because collecting human feedback and refining the model with reinforcement learning is repeated for continuous improvement.
What are the challenges and limitations of RLHF?
There are some challenges and limitations to RLHF, including the following:
- Subjectivity and human error. The quality and feedback response can vary between users and testers. When generating answers to advanced inquiries, people with the proper background in complex fields, such as science or medicine, should provide feedback. However, finding experts can be expensive and time-consuming.
- Wording of questions. The quality of the answers depends on the queries. An AI agent cannot decipher user intent without the proper wording used in training -- even with significant RLHF training. Because of the lack of understanding of context, RLHF responses can be incorrect. Sometimes, this can be solved by rephrasing the question.
- Training bias. RLHF is prone to problems with machine learning bias. Asking a factual question, such as "What does 2+2 equal?" gives one answer. However, more complex questions, such as those that are political or philosophical in nature, can have several answers. AI defaults to its training answer, causing bias since there may be other answers.
- Scalability. Because this process uses human feedback, it can be more time-consuming.
Scaling the process to train bigger, more sophisticated models can be time- and resource-intensive because it depends on human feedback. This problem might be solved by creating techniques for automating or semiautomating the feedback process.
Implicit language Q-learning implementation
LLMs can be inconsistent in their accuracy for some user-specified tasks. A method of reinforcement learning called implicit language Q-learning (ILQL) addresses this.
Traditional Q-learning algorithms use language to help the agent understand the task. ILQL is a type of reinforcement learning algorithm that is used to teach an agent to perform a specific task, such as training a customer service chatbot to interact with a customer.
In ILQL, the agent receives a reward based on the outcome and human feedback. The agent then uses this reward to update its Q-values, which are used to determine the best action to take in the future. In traditional Q-learning, the agent receives a reward only for the action outcome.
ILQL is an algorithm to teach agents to perform complex tasks with the help of human feedback. Using human input in the learning process, agents can be trained more efficiently than by self-learning alone.