OpenAI\'s Iterative Approach to AI Alignment

OpenAI's Approach to AI Alignment

OpenAI's approach to AI alignment is iterative and empirical. We strive to align highly capable AI systems through experimentation, learning from successes and failures to refine our techniques and build safer, more aligned systems. This involves three key areas: 1)refining AI training using human feedback, 2)developing methods for AI to assist in human evaluation, and 3)creating AI systems capable of contributing to alignment research itself. Each of these areas is approached iteratively, allowing us to learn and adapt our methods continuously. You can read more about this process in our detailed blog post on alignment research.

Training AI with Human Feedback: This core method uses Reinforcement Learning from Human Feedback (RLHF). We train models to better understand and follow human intent, incorporating aspects like truthfulness and safety. While effective with current models like InstructGPT, as detailed here, scaling this approach to future, more capable AI systems remains a challenge.

AI-Assisted Human Evaluation: As AI capabilities advance, human evaluation becomes increasingly difficult. We are developing methods leveraging AI to assist humans in evaluating AI outputs, tackling complex tasks beyond human capacity. This includes using AI to summarize or analyze results to help humans identify flaws, reducing the limitations of human oversight. More details on this approach are available in our research blog.

AI-Driven Alignment Research: Ultimately, we aim to create AI systems capable of conducting alignment research themselves, accelerating progress beyond human capabilities. We believe that evaluating alignment research is easier than producing it; thus, human researchers will focus more on reviewing AI-generated research. This ambitious goal has its inherent limitations. The challenges associated with this step, along with those of the other two, are explored further in our research blog.

Limitations: Our approach faces significant limitations. These include the potential for bias amplification through AI assistance, the challenge of ensuring the alignment of increasingly complex AI systems, and the possibility that fundamentally different methods will be required for aligning future, far more capable AI. These challenges are discussed in more detail in our blog post.