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 drew critical media coverage. The paper withheld critical methodology steps and most inputs needed to reproduce results. Our meta-analysis shows how two separate evaluations filled in the gaps and demonstrated that Google RL lags behind (i) human designers, (ii) a well-known algorithm (Simulated Annealing), and (iii) generally-available commercial software, while being slower; and in a 2023 open research contest, RL methods weren't in top 5. Crosschecked data indicate that the integrity of the Nature paper is substantially undermined owing to errors in conduct, analysis and reporting. Before publishing, Google rebuffed internal allegations of fraud. We note policy implications and conclusions for chip design.

翻译：强化学习在硅芯片物理设计中的应用（见2021年谷歌发表在《自然》杂志的论文）因其文献记录不充分且引发争议的主张而激起波澜，并招致了批判性媒体关注。该论文隐去了大部分再现结果所需的关键方法论步骤和输入信息。我们的综合分析表明，两项独立评估填补了这些空白，并证明谷歌强化学习方法在以下方面均落后：（i）人类设计师；（ii）经典算法（模拟退火法）；（iii）市场上通用的商业软件，同时其运行速度更慢；而在2023年的一项公开研究竞赛中，强化学习方法未能进入前五名。交叉验证数据显示，由于实验操作、分析与报告中的错误，《自然》论文的完整性受到严重损害。在发表前，谷歌驳回了内部对其存在学术不端的指控。我们指出了对芯片设计领域的政策启示与相关结论。