Link to original article

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: How much alignment data will we need in the long run?, published by Jacob Hilton on August 10, 2022 on The AI Alignment Forum. This question stands out to me because: It should directly affect empirical alignment priorities today While it is informed by both theoretical and empirical evidence, it seems tractable for purely theoretical alignment researchers to make progress on today It's even possible that theoretical alignment researchers already consider this to be a solved problem, in which case I think it would be valuable to have a carefully-reasoned write-up that empirical alignment practitioners can feel confident in the conclusions of. Thanks to Paul Christiano for discussion that prompted this post and to Jan Leike for comments. Why this should affect empirical alignment priorities today Outer alignment can be framed as a data quality problem. If our alignment training data correctly favors aligned behavior over unaligned behavior, then we have solved outer alignment. But if there are errors in our data that cause an unaligned policy to be preferred, then we have a problem. It is common to worry about errors in the alignment training data that arise from evaluation being too difficult for humans. I think this makes sense for two reasons: Firstly, errors of this kind specifically incentivize models to deceive the human evaluators, which seems like an especially concerning variety of alignment failure. Secondly, errors of this kind will get worse with model capability, which is a scary dynamic: models would get more misaligned as they became more powerful. Nevertheless, I think we could still get catastrophic alignment failures from more mundane kinds of data quality issues. If we had the perfect scalable alignment solution, but the humans in the loop simply failed to implement it correctly, that could be just as bad as not using the solution at all. But prevention of mundane kinds of data quality issues could look very different depending on the amount of data being collected: If a large amount of alignment training data is needed, then a significant amount of delegation will be required. Hence practitioners will need to think about how to choose who to delegate different tasks to (including defending against adversaries intentionally introducing errors), how to conduct quality control and incentivize high data quality, how to design training materials and interfaces to reduce the likelihood of human error, and so on. If only a small amount of alignment training data is needed, then it will be more feasible to put a lot of scrutiny on each datapoint. Perhaps practitioners will need to think about how to appropriately engage the public on the choice of each datapoint in order to maintain public trust. Hence settling the question of how much alignment training data we will need in the long run seems crucial for deciding how much empirical alignment efforts should invest in the first versus the second kind of effort. In practice, we may collect both a larger amount of lower-quality data and a smaller amount of higher-quality data, following some quality-quantity curve. The generalized form of the question then becomes: what is the probability of alignment for a given quality-quantity curve? Practitioners will then be able to combine this with feasibility considerations to decide what curve to ultimately follow. Initial thoughts on this question Considerations in favor of less alignment training data being required: Larger models are more sample-efficient than smaller models, especially in the presence of pre-training. Hence for a given task we should expect the amount of alignment training data we need to go down over time. There could be many rounds of fine-tuning used to teach models the precise details of performing certain tasks, and data quality may only be ...