NEW CriticGPT by OpenAI: RLHF + FSBS

OpenAI has introduced a new methodology to enhance the optimization of large language models (LLMs) using reinforcement learning from human feedback (RLHF) combined with Force Sampling Beam Search (FSBS). This technique is encapsulated in a new model called CriticGPT, designed to critique other AI models’ outputs, specifically targeting the alignment phase of training. This model aids human trainers by evaluating and critiquing the AI’s performance, helping to catch errors and improve the quality of AI-generated content. The video explains the necessity of CriticGPT due to the increasing complexity of AI models, which require PhD-level knowledge for effective evaluation. OpenAI’s research shows that CriticGPT significantly reduces the rate of hallucinations and nitpicking compared to standard models. The video also delves into the training process of CriticGPT, which involves human trainers inserting errors into code and then using these examples to train the model. The ultimate goal is to create a system where AI can assist humans in improving their performance, thus making the training process more cost-effective and efficient. The video concludes by discussing the broader implications of this technology and how it could revolutionize the way we improve AI systems.