As the field of artificial intelligence continues to advance, OpenAI (an AI research and deployment company) is addressing one of the core challenges with AI — aligning future superhuman AI systems with human values.

This task is made even more difficult by the fact that humans will need to supervise AI systems that are much smarter than they are. However, a recent study has found a potential solution to this problem by exploring a simple analogy: can small models supervise large models?

The study showed that a GPT-2-level model can elicit most of GPT-4’s capabilities, performing at a level close to GPT-3.5 even on difficult problems where the small model failed. This discovery opens up a new research direction, allowing researchers to make iterative empirical progress today while directly tackling the central challenge of aligning future superhuman models.

The Superalignment Problem

The Superalignment team was formed earlier this year to address the problem of superintelligence alignment. The team believes that within the next ten years, superintelligence – artificial intelligence that is vastly smarter than humans – could be developed. However, there is still no reliable way to steer and control superhuman AI systems. This poses a significant risk to humanity, as even the most advanced AI systems could become dangerous.

The team’s first paper, which introduces a new research direction for empirically aligning superhuman models, has been released. Current alignment methods, such as reinforcement learning from human feedback (RLHF), rely on human supervision. However, future AI systems will be capable of extremely complex and creative behaviors that will make it difficult for humans to supervise them reliably.

Superhuman models may be able to write millions of lines of novel and potentially dangerous computer code that would be challenging even for expert humans to understand. Humans will be “weak supervisors” relative to superhuman AI models, which is a core challenge for AGI alignment. The question remains: how can weak supervisors trust and control substantially stronger models?

To solve the superalignment problem, the Superalignment team is exploring new research directions that go beyond current alignment methods. The team’s goal is to ensure that even the most advanced AI systems in the future remain safe and beneficial to humanity.

Our Setup

The proposed analogy for superalignment involves using a smaller model to supervise a larger model. This concept is not new in traditional machine learning, where humans supervise AI systems that are weaker than themselves. However, to align superintelligence, humans will need to supervise AI systems that are smarter than them.

The critical question is whether the strong model will generalize according to the weak supervisor’s underlying intent, leveraging its full capabilities to solve the task even on difficult problems where the weak supervisor can only provide incomplete or flawed training labels.

To study this problem, the team will use a small model to supervise a larger model and evaluate its performance. The small model will provide training signals to the larger model, and the larger model will learn from these signals to perform a given task. The team will then evaluate the larger model’s performance on the same task without the supervision of the small model.

The team will use pre-trained models with excellent raw capabilities, which do not require teaching new tasks from scratch. The small model will provide incomplete or flawed training labels, and the larger model will need to generalize according to the underlying intent of the weak supervisor.

The team will compare the performance of the larger model with and without the supervision of the small model to determine whether the small model can effectively supervise the larger model. This study will provide insights into the feasibility of using a smaller model to supervise a larger model and align superintelligence.

Our Results

The study aimed to improve the generalization of GPT-4 in various settings using a GPT-2-level model as a weak supervisor. The team utilized a simple method that promotes the strong model to be more confident, even if it means disagreeing with the weak supervisor. The results showed that when GPT-4 was supervised using this method on NLP tasks, the model typically performed between GPT-3 and GPT-3.5. This indicates that much of GPT-4’s capabilities can be recovered with only weaker supervision.

The study also found that the method had important limitations, such as not being effective on ChatGPT preference data. However, the team identified other approaches that showed promise, such as optimal early stopping and bootstrapping from small to intermediate to large models.

Overall, the results suggest that naive human supervision, such as reinforcement learning from human feedback, may not scale well to superhuman models without further work. However, it is feasible to substantially improve weak-to-strong generalization. The team’s approach provides a proof of concept, and further research can build upon these findings to advance the field of NLP.

In summary, the study’s results demonstrate the potential to significantly improve the generalization of language models with weaker supervision, and the team’s approach could pave the way for future advancements in NLP.

Research Opportunities

The problem of aligning superhuman models is a difficult challenge that requires empirical research to make progress. While there are still disanalogies between the current empirical setup and the ultimate problem of aligning superhuman models, there are promising directions for future work that can help overcome these difficulties.

To facilitate more research in this area, a $10 million grants program has been launched for graduate students, academics, and other researchers to work on superhuman AI alignment broadly. The program is particularly interested in supporting research related to weak-to-strong generalization.

In addition, open-source code has been released to make it easy for researchers to get started with weak-to-strong generalization experiments. This initiative is an exciting opportunity for the ML research community to make progress on alignment.

To advance the scientific understanding of when and how good weak-to-strong generalization can be expected, better scalable methods need to be developed. This will enable researchers to fix the disanalogies in the current setup and start making empirical progress on aligning future superhuman models.

Overall, the initiative to align future superhuman AI systems to be safe is an important problem that requires empirical progress. The grants program and open-source code release are exciting opportunities for researchers to make breakthroughs in this area.

Frequently Asked Questions

What are the limitations of superhuman AI systems in transitioning from weak to strong generalization?

Superhuman AI systems face limitations in transitioning from weak to strong generalization due to their inability to handle unexpected scenarios. These systems are trained on a specific dataset, and when faced with new data that is not similar to the training set, they struggle to generalize and make accurate predictions. Additionally, these systems are limited by their inability to reason about causality and make decisions based on ethical considerations.

How do superhuman AI systems overcome the challenge of domain adaptation?

Superhuman AI systems overcome the challenge of domain adaptation by utilizing transfer learning. Transfer learning involves using knowledge gained from one domain to improve performance in another domain. By leveraging pre-trained models and fine-tuning them with new data, these systems can adapt to new domains and improve their generalization capabilities.

What are the current benchmarks used to measure the generalization capabilities of AI systems?

The current benchmarks used to measure the generalization capabilities of AI systems include the ImageNet dataset, the COCO dataset, and the CIFAR-10 dataset. These datasets are commonly used in computer vision tasks and provide a standardized way to evaluate the performance of AI systems.

In what ways can superhuman AI systems be improved to handle unexpected scenarios better?

Superhuman AI systems can be improved to handle unexpected scenarios better by incorporating techniques such as adversarial training and robust optimization. Adversarial training involves training the system on examples that are intentionally designed to deceive it, which can improve its ability to handle unexpected scenarios. Robust optimization involves optimizing the system to perform well across a range of possible scenarios, rather than just the training set.

What role does transfer learning play in enhancing the generalization of superhuman AI?

Transfer learning plays a crucial role in enhancing the generalization of superhuman AI by allowing models to leverage knowledge gained from one domain to improve performance in another domain. By fine-tuning pre-trained models with new data, these systems can adapt to new domains and improve their generalization capabilities.

How does the complexity of tasks affect an AI system’s ability to generalize from weak to strong?

The complexity of tasks can affect an AI system’s ability to generalize from weak to strong by increasing the difficulty of learning representations that generalize well to new scenarios. As tasks become more complex, the number of possible scenarios that the system may encounter increases, making it more challenging to generalize from the training set to new data. However, by incorporating techniques such as transfer learning and robust optimization, these systems can overcome these challenges and improve their generalization capabilities.