Value Generalisation as the Alignment Path
π‘A unified theory reframes many alignment failures as one problem: values not generalising to novel situations.
β‘ 30-Second TL;DR
What Changed
The article introduces a theory of change centered on value generalisation as the route to AI alignment.
Why It Matters
If this framing is correct, alignment research should focus more explicitly on testing whether models retain human-intended values under distribution shift, rather than relying primarily on performance in familiar evaluation settings. It also offers a unifying lens for comparing otherwise distinct alignment failure modes.
What To Do Next
Add distribution-shifted value-generalisation tests to your alignment evaluation suite, checking whether model behavior preserves the intended policy in scenarios absent from training data.
Key Points
- β’The article introduces a theory of change centered on value generalisation as the route to AI alignment.
- β’It claims that insufficient value generalisation is a fundamental contributor to alignment difficulty.
- β’Many AI alignment failure modes are framed as failures to preserve intended values in novel contexts.
- β’The approach connects the challenge of value generalisation with broader arguments about why alignment is inherently difficult.
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Original source: AI Alignment Forum β
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