New AI goal transformation promotes corrigibility without performance loss

Researchers have developed a "Corrigibility Transformation" to create AI goals that are open to updates without sacrificing performance. This method involves predicting rewards based on preventing updates, which then guides the AI's actions. Empirically, this approach has demonstrated corrigible behavior in gridworld simulations and for language models when applied at the prompt level. AI

IMPACT Introduces a novel method for enhancing AI safety by ensuring models can accept goal updates without performance degradation.

RANK_REASON The cluster contains an academic paper detailing a new method for AI safety. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• CatalyzeX
• Corrigibility Transformation
• DagsHub
• Gotit.pub
• Hugging Face
• Rubi Hudson
• ScienceCast

• paper
• safety

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