Reinforcement learning (RL) for physical design of silicon chips in a Google 2021 Nature paper stirred controversy due to poorly documented claims that raised eyebrows and attracted critical media coverage. The Nature paper withheld most inputs needed to produce reported results and some critical steps in the methodology. But two independent evaluations filled in the gaps and demonstrated that Google RL lags behind human designers, behind a well-known algorithm (Simulated Annealing), and also behind generally-available commercial software. Crosschecked data indicate that the integrity of the Nature paper is substantially undermined owing to errors in the conduct, analysis and reporting.

翻译：针对硅芯片物理设计的强化学习（RL）在谷歌2021年发表于《自然》期刊的论文中引发争议，其因缺乏充分记录的主张令人质疑，并招致批评性媒体关注。该论文隐瞒了再现所报告结果所需的大部分输入数据以及方法论中的若干关键步骤。然而，两项独立评估填补了这些空白，证明谷歌的强化学习不仅落后于人类设计师，也落后于一种广为人知的算法（模拟退火），甚至还不及通常可用的商业软件。交叉验证的数据表明，由于在研究实施、分析与报告环节存在错误，该《自然》论文的学术完整性已受到严重损害。