Anthropic 公佈一項研究，探索利用 Claude 代理自動化紅隊測試，以發現微妙的新興不對齊行為。
- 方法：部署數千個 Claude 代理，讓其嘗試繞過訓練資料監測器，並生成看似無害但實際教導不對齊行為的資料。
New Anthropic research: Automating subtle emergent misalignment research.
Red-teaming is a vital part of model safety testing, but it’s also limited by researchers’ time. To see if we could automate it, we set thousands of Claude agents the task of evading a training data monitor.
These agents developed training data that looked harmless to monitors (like a veteran wrestler mentoring a newcomer, or a Q&A about professional beekeeping), but that in fact taught misaligned behavior.
Examples like these can be used to directly improve future training data monitors. This research is only a first step, but it suggests that future red-teaming efforts could improve much further, using better models and more compute.
Read the full post on the Alignment Science Blog: https://t.co/24Gs1oSw7Q