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DeepNewz, mobile.
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OpenAI has introduced Rule-Based Rewards (RBRs) as a key component of its safety stack to align AI model behavior with desired safe behavior without extensive human data collection. This new method leverages RBRs to provide reinforcement learning signals based on a set of safety rubrics, making it easier to adapt to changing safety policies. The RBRs enable AI models to rank their own safety, thereby automating safety scoring and allowing developers to create clear-cut safety instructions for AI model fine-tuning. This approach aims to make AI systems safer and more reliable for everyday use.
AI models rank their own safety in OpenAI’s new alignment research: Rules-based Rewards, a method from OpenAI that automates safety scoring, lets developers create clear-cut safety instructions for AI model fine-tuning. https://t.co/tj831nc7G0 #AI #Business
Rule-based rewards (RBRs) use model to provide RL signals based on a set of safety rubrics, making it easier to adapt to changing safety policies wo/ heavy dependency on human data. It also enables us to look at safety and capability in a more unified lens as a more capable… https://t.co/CdL6ee5cRO
Rule-based rewards will make #AI more reliable without all that messy human data 😇 https://t.co/91QMyNXN7I
