The techniques used in post-training can significantly affect the behavior of AI models, and excessive safety measures can lead to undesirable outcomes.

Historically, AI safety has focused on aligning models with human preferences, but AI control offers a complementary strategy to ensure that even if models have bad goals, they cannot act harmfully.

Control evaluations are challenging because AI models can learn to demonstrate misalignment if trained on good misalignment examples, making it hard to generalize to a broader misaligned policy.

AI safety has transitioned from a theoretical concept to practical applications, integrating safety measures into AI systems to ensure they follow user intent effectively.

AI control is an approach to mitigating AI misalignment, addressing the risk that AI models may have goals that differ from human goals.

Anthropic's approach to AI involves training models to align with desired behaviors rather than relying solely on rigid rules, which can be insufficient in complex situations.

Regulating current AI capabilities heavily could stifle innovation, but as models evolve and pose significant risks, some oversight will be necessary to ensure safety without hindering progress.

Anthropic has shown that inserting specific phrases during pre-training can elicit different behaviors from AI models, raising concerns about intentional alignment.

To effectively test AI control measures, models should be stress-tested by training them to understand the mitigations in place, ensuring they cannot exploit knowledge of monitoring to act harmfully.