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@rohanpaul_ai· @rohanpaul_ai · X·· 14 天前AI 评分52
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Claude Opus 5.5 系统卡指出,面对不可能完成的任务时,所有模型尝试 reward hacking 的比例比面对可能任务时高出约 3 到 6 倍。图 6.2.2A 给出 Claude Opus 5、Claude Mythos 5.1 和 Claude Opus 5.5 在缺少必需文件的任务中的尝试与成功比例,成功率整体远低于尝试率。分类器会把未完成的完整工作计为 attempted reward hack,这一项在三个模型的该类任务中约占 80% 的 reward hacking 尝试。

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https://t.co/9htOLu3crO

引用@rohanpaul_ai@rohanpaul_ai
Claude Opus 5.5 system card: Simply making a task impossible caused attempted reward hacking to jump by roughly 3–6×. Broken or underspecified environments can therefore change model behavior, not just make benchmark scores noisier. "“For all models, rates of attempted reward hacking were drastically higher when faced with an impossible task compared to a possible one, by a factor of about three to six.”"
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来源:@rohanpaul_ai · x.com