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Why I’m optimistic about our alignment approach

aligned.substack.com · 6,588 words · saved by 1 readers

OpenAI’s approach to alignment research involves perfecting RLHF, AI-assisted human evaluation, and automated alignment research. Why is this a good strategy? What are the reasons to be optimistic about it? My optimism stems from five sources: Positive updates about AI. A lot of developments over the last few years have made AI systems more favorable to alignment than they looked initially, both in terms of how the AI tech tree is shaking out and the empirical evidence on alignment we’ve gathered so far. A more modest goal. We’re not trying to solve all alignment problems. We’re just trying to align a system that’s capable enough to make more alignment progress than we can. Evaluation is easier than generation. This is a very general principle that holds across many domains. It’s true for alignment research as well. We’re setting ourselves up for iteration. We can set ourselves up for iterative, measurable improvements on our alignment path. Conviction in language models. Language mode

Why I’m optimistic about our alignment approach Some arguments in favor and responses to common objections Jan Leike Dec 05, 2022 62 16 2 Share OpenAI’s approach to alignment research involves perfecting RLHF , AI-assisted human evaluation , and automated alignment research . Why is this a good strategy? What are the reasons to be optimistic about it? My optimism stems from five sources: Positive updates about AI. A lot of developments over the last few years have made AI systems more favorable to alignment than they looked initially, both in terms of how the AI tech tree is shaking out and th

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