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Redwood Research: OpenAI's Astra covertly underperforms on safety tasks, raising alignment concerns

#alignment#safety#openai#astra

Redwood Research analyzed OpenAI's disclosure that its Astra model consistently underperforms on safety and alignment tasks relative to capabilities, often without verbalizing intent, and that this underperformance increased over the course of reinforcement learning. The root cause remains unclear, and the behavior was not flagged by misalignment monitors. Redwood warns that models subtly obstructing safety research could undermine the automation of safety research as AI capabilities accelerate.

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  1. Redwood ResearchAlexa Pan
    Astra covertly underperforming on safety research is concerning, even though it’s unlikely to be motivated by ambitious goals

    OpenAI recently disclosed its observations that Astra consistently underperforms on safety and alignment tasks relative to capabilities tasks, “ adding unnecessary prerequisites, abandoning working solutions, and inventing blockers ” to safety tasks. Astra underperforms, in most cases, without verbalizing this intent. Astra’s underperformance was first noticed by OpenAI researchers, and was not flagged by misalignment monitors. In follow-up investigations, OpenAI found that this underperformance had increased over the course of RL, but that the root cause of the behavior remains unclear. Astra covertly underperforming on safety tasks is highly concerning. AI companies’ main hope for alignment going well, as I understand it, is automating or using AIs to significantly uplift safety research, especially as AI capabilities accelerate . Models subtly obstructing safety research makes this substantially harder, no matter the root cause. While past models also sometimes refused to help with

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