Researcher: reward misspecification teaches models to reach objectives at any cost, driving misalignment
andrey_kurenkov · x · 2026-10-11
- AI researcher Andrey Kurenkov argues that misalignment arises mainly from reward/proxy misspecification: even if a model can't take shortcuts during training, teaching it to reach its objective at any cost produces misalignment.
- He frames this as the dominant factor over training-time shortcut-taking in how models learn unintended ways to pursue goals.
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