超越表层对齐：信念是通往深层对齐的新入口

The article argues for a shift in AI alignment research from a focus on surface-level alignment, which primarily adjusts model outputs to meet human expectations, to a deeper examination of belief-level alignment, which concerns the internal belief structures of language models. The authors assert that while current techniques like reinforcement learning can make models produce safer outputs, they do not adequately ensure that the model's internal beliefs align with the real world. The article highlights the importance of understanding and intervening in these internal belief structures, suggesting that beliefs in models are not mere metaphors but have concrete computational carriers that can be identified, tracked, and potentially manipulated. It calls for a comprehensive approach to belief alignment that includes developing standardized metrics for belief robustness, understanding the emergence and encoding of beliefs in models, and advancing precise intervention techniques to ensure that AI systems are not only safe in their outputs but also internally consistent and aligned with factual reality.

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