Artificial intelligence (AI) has gained spots in many industries. A survey conducted by IBM reported that about 42% of companies with more than 1,000 employees deploy AI in their businesses. This is a large proportion and it is expected that more companies will explore the benefits of this technology in their business.
However, to implement AI, you must plan to do it as expected to ensure that your business will benefit from its use. This often requires a type of machine learning, such as human feedback reinforcement learning and RLHF, which allows machines to learn from both trial and error and human guidance. The RLHF approach improves AI from chatbots and voice to text systems to ensure understanding and response.
Knowing how to effectively train AI through RLHF will help businesses in the long term, especially when this type of technology is used more frequently and in more sectors.
1 – Start with a pre-trained model
To get started, we recommend using a pre-trained model. This means that the model already has a broad understanding of the fundamentals, including language, but no specialization. What we’re trying to build here is specialization. Starting with a pre-trained model is a great advantage.
Pre-trained models already understand a large amount of data, thus storing the time and resources needed for the initial training state. This will make the next step more focused and the training is specific.
2 – Supervise the fine adjustments
After selecting a pre-trained model, the next step is where this model will receive additional training for a particular domain or task. The data labeled at this stage is used to help the AI model create accurate and relevant outputs. This is where human guidance begins.
Human judgment guides the model towards preferred behaviors and responses. The person responsible for training should be careful when selecting data so that the model adapts to the specific requirements of the task.
3 – Train another model for the desired output
The third step in training AI is to train another model to identify and reward the generated desired output. However, some approaches integrate reward modeling into the overall training loop.
Once completed separately, this AI model evaluates the results and scores them based on specific criteria such as accuracy, alignment with preferred output, and relevance. The given score serves as guidance to generate a better response.
4 – Use reinforcement learning via PPO
The AI model then passes through PPO-mediated reinforcement learning, also known as proximal policy optimization. This widely used complex algorithmic approach is important in interactive machine learning to enable models to be trained when interacting with the environment in real time. Compared to previous methods, it has been proven to use training data better.
The decision-making process is honeeded by reward and forfeiture, allowing for learning and adaptation. PPOs are used by AI assistants, chatbots, robots, and more.
5 – Test in the real world
Once all the other steps are complete, it’s time to test your real-world AI systems. Often, this is a selected group of individuals to assess and challenge the system in a variety of situations. Tested in responses to help you deal with real features and unexpected scenarios.
These individuals test the AI system to ensure it works correctly and ensure it is completely ethical in its sequence. This includes potential harm, regulatory compliance, and bias in training data.
Through this post, and with a better understanding of RLHF, it should become very clear how important human involvement is in the development of artificial intelligence. Without human input, AI will not adapt to society’s values or become intelligent. Therefore, it is essential to utilize human guidance when developing AI systems.
February 21, 2025
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